{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ssms\n",
    "from matplotlib import pyplot as plt\n",
    "from ssms.basic_simulators.simulator import simulator\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'out' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb Cell 2\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#X51sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m np\u001b[39m.\u001b[39munique(out[\u001b[39m\"\u001b[39m\u001b[39mmetadata\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m\"\u001b[39m\u001b[39mt\u001b[39m\u001b[39m\"\u001b[39m])\u001b[39m.\u001b[39mshape\n",
      "\u001b[0;31mNameError\u001b[0m: name 'out' is not defined"
     ]
    }
   ],
   "source": [
    "np.unique(out[\"metadata\"][\"t\"]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulations = simulator(\n",
    "    model=\"angle_deadline\",\n",
    "    theta={\n",
    "        \"a\": np.ones(10000) * 1.0,\n",
    "        \"z\": np.ones(10000) * 0.5,\n",
    "        \"t\": np.ones(10000) * 0.5,\n",
    "        \"v\": np.ones(10000) * 1.0,\n",
    "        \"theta\": np.ones(10000) * 0.5,\n",
    "        \"deadline\": np.ones(10000) * 5.0,\n",
    "    },\n",
    "    n_samples=1,\n",
    "    max_t=20,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [0.6739382 ],\n",
       "        [0.7260139 ],\n",
       "        ...,\n",
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       "        [0.90793693],\n",
       "        [0.86936617]], dtype=float32),\n",
       " 'choices': array([[ 1],\n",
       "        [ 1],\n",
       "        [-1],\n",
       "        ...,\n",
       "        [ 1],\n",
       "        [ 1],\n",
       "        [-1]], dtype=int32),\n",
       " 'metadata': {'v': array([1., 1., 1., ..., 1., 1., 1.], dtype=float32),\n",
       "  'a': array([1., 1., 1., ..., 1., 1., 1.], dtype=float32),\n",
       "  'z': array([0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5], dtype=float32),\n",
       "  't': array([0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5], dtype=float32),\n",
       "  's': 1.0,\n",
       "  'deadline': array([5., 5., 5., ..., 5., 5., 5.], dtype=float32),\n",
       "  'theta': array([0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5], dtype=float32),\n",
       "  'delta_t': 0.0010000000474974513,\n",
       "  'max_t': 20.0,\n",
       "  'n_samples': 1,\n",
       "  'n_trials': 10000,\n",
       "  'simulator': 'ddm_flexbound',\n",
       "  'boundary_fun_type': 'angle',\n",
       "  'possible_choices': [-1, 1],\n",
       "  'trajectory': array([[ 0.0000000e+00],\n",
       "         [ 3.4604587e-02],\n",
       "         [ 9.1727227e-02],\n",
       "         ...,\n",
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       "  'boundary': array([ 1.       ,  0.9994537,  0.9989074, ..., -9.924958 , -9.925504 ,\n",
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       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00],\n",
       "         [0.000e+00, 0.000e+00]]])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"ddm_boundaryfun_driftfun_deadline\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rts': array([[1.2078639 ],\n",
       "        [1.0145779 ],\n",
       "        [0.824644  ],\n",
       "        ...,\n",
       "        [0.87732315],\n",
       "        [1.1183238 ],\n",
       "        [1.1726061 ]], dtype=float32),\n",
       " 'choices': array([[ 1],\n",
       "        [ 1],\n",
       "        [ 1],\n",
       "        ...,\n",
       "        [ 1],\n",
       "        [-1],\n",
       "        [ 1]], dtype=int32),\n",
       " 'metadata': {'v': array([1., 1., 1., ..., 1., 1., 1.], dtype=float32),\n",
       "  'a': array([1., 1., 1., ..., 1., 1., 1.], dtype=float32),\n",
       "  'z': array([0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5], dtype=float32),\n",
       "  't': array([0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5], dtype=float32),\n",
       "  's': 1.0,\n",
       "  'deadline': array([999., 999., 999., ..., 999., 999., 999.], dtype=float32),\n",
       "  'theta': array([0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5], dtype=float32),\n",
       "  'delta_t': 0.0010000000474974513,\n",
       "  'max_t': 20.0,\n",
       "  'n_samples': 1,\n",
       "  'n_trials': 10000,\n",
       "  'simulator': 'ddm_flexbound',\n",
       "  'boundary_fun_type': 'angle',\n",
       "  'possible_choices': [-1, 1],\n",
       "  'trajectory': array([[ 0.0000000e+00],\n",
       "         [ 4.4858374e-02],\n",
       "         [-3.6620833e-03],\n",
       "         ...,\n",
       "         [-9.9900000e+02],\n",
       "         [-9.9900000e+02],\n",
       "         [-9.9900000e+02]], dtype=float32),\n",
       "  'boundary': array([ 1.       ,  0.9994537,  0.9989074, ..., -9.924958 , -9.925504 ,\n",
       "         -9.92605  ], dtype=float32),\n",
       "  'model': 'angle'}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_dict = {}\n",
    "out_dict[\"choice_p\"] = {}\n",
    "out_dict[\"choice_p_no_omission\"] = {}\n",
    "out_dict[\"p_omission\"] = {}\n",
    "for choice in simulations[\"metadata\"][\"possible_choices\"]:\n",
    "    out_dict[\"choice_p\"][choice] = np.array(\n",
    "        [\n",
    "            (simulations[\"choices\"] == choice).sum()\n",
    "            / simulations[\"choices\"].flatten().shape[0]\n",
    "        ]\n",
    "    )\n",
    "    out_dict[\"choice_p_no_omission\"][choice] = np.array(\n",
    "        [\n",
    "            (simulations[\"choices\"][simulations[\"rts\"] != -999] == choice).sum()\n",
    "            / simulations[\"choices\"].flatten().shape[0]\n",
    "        ]\n",
    "    )\n",
    "    out_dict[\"p_omission\"][choice] = np.array(\n",
    "        [(simulations[\"rts\"] == -999).sum() / simulations[\"choices\"].flatten().shape[0]]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'simulations' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#Y124sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m simulations[\u001b[39m'\u001b[39m\u001b[39mrts\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m!=\u001b[39m \u001b[39m-\u001b[39m\u001b[39m999\u001b[39m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'simulations' is not defined"
     ]
    }
   ],
   "source": [
    "simulations[\"rts\"] != -999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'choice_p': {-1: array([0.2386]), 1: array([0.7614])},\n",
       " 'choice_p_no_omission': {-1: array([0.0895]), 1: array([0.4996])},\n",
       " 'p_omission': {-1: array([0.4109]), 1: array([0.4109])}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'v': array([1., 1., 1., ..., 1., 1., 1.], dtype=float32),\n",
       " 'a': array([1.5, 1.5, 1.5, ..., 1.5, 1.5, 1.5], dtype=float32),\n",
       " 'z': array([0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5], dtype=float32),\n",
       " 't': array([0.5, 0.5, 0.5, ..., 0.5, 0.5, 0.5], dtype=float32),\n",
       " 's': 1.0,\n",
       " 'deadline': array([1., 1., 1., ..., 1., 1., 1.], dtype=float32),\n",
       " 'delta_t': 0.0010000000474974513,\n",
       " 'max_t': 20.0,\n",
       " 'n_samples': 1,\n",
       " 'n_trials': 10000,\n",
       " 'simulator': 'ddm_flexbound',\n",
       " 'boundary_fun_type': 'angle',\n",
       " 'possible_choices': [-1, 1],\n",
       " 'trajectory': array([[ 0.000000e+00],\n",
       "        [-2.215560e-02],\n",
       "        [-4.701581e-03],\n",
       "        ...,\n",
       "        [-9.990000e+02],\n",
       "        [-9.990000e+02],\n",
       "        [-9.990000e+02]], dtype=float32),\n",
       " 'boundary': array([  1.5      ,   1.4984426,   1.4968852, ..., -29.645042 ,\n",
       "        -29.646599 , -29.648155 ], dtype=float32),\n",
       " 'model': 'angle'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out[\"metadata\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/ssms/basic_simulators/simulator.py:377: UserWarning: Deadline model request, and theta not supplied as dict.Make sure to supply the deadline parameters in last position!\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from copy import deepcopy\n",
    "\n",
    "v = 1.0\n",
    "a = 2.0\n",
    "z = 0.5\n",
    "t = 0.0\n",
    "theta = 0.7\n",
    "deadline = 10\n",
    "out = simulator(\n",
    "    model=\"angle_deadline\",\n",
    "    theta=[v, a, z, t, theta, deadline],\n",
    "    n_samples=10000,\n",
    "    max_t=20,\n",
    ")\n",
    "out_log = deepcopy(out)\n",
    "out_log[\"log_rts\"] = np.ones(out[\"rts\"].shape) * -999\n",
    "out_log[\"log_rts\"][out_log[\"rts\"] != -999] = np.log(\n",
    "    out_log[\"rts\"][out_log[\"rts\"] != -999]\n",
    ")\n",
    "del out_log[\"rts\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.00312946, 0.00521577, 0.00834523, 0.02190623, 0.02920831,\n",
       "        0.03442408, 0.040683  , 0.06363239, 0.07510708, 0.07197762,\n",
       "        0.08032285, 0.07510708, 0.08866808, 0.067805  , 0.08449547,\n",
       "        0.07093446, 0.05111454, 0.05215769, 0.04902823, 0.02399254,\n",
       "        0.01147469, 0.00625892, 0.00312946, 0.        , 0.        ,\n",
       "        0.        , 0.00312946, 0.03442408, 0.22010547, 0.49654125,\n",
       "        0.71247411, 0.87520611, 0.93153642, 0.90545758, 0.91797542,\n",
       "        0.83660942, 0.70830149, 0.58103672, 0.50071387, 0.44438356,\n",
       "        0.36719017, 0.27643578, 0.21906232, 0.14499839, 0.11579008,\n",
       "        0.06676185, 0.03129462, 0.01043154, 0.00938839, 0.00417262]),\n",
       " array([-2.40115374, -2.30529061, -2.20942747, -2.11356434, -2.01770121,\n",
       "        -1.92183808, -1.82597494, -1.73011181, -1.63424868, -1.53838554,\n",
       "        -1.44252241, -1.34665928, -1.25079614, -1.15493301, -1.05906988,\n",
       "        -0.96320675, -0.86734361, -0.77148048, -0.67561735, -0.57975421,\n",
       "        -0.48389108, -0.38802795, -0.29216481, -0.19630168, -0.10043855,\n",
       "        -0.00457542,  0.09128772,  0.18715085,  0.28301398,  0.37887712,\n",
       "         0.47474025,  0.57060338,  0.66646652,  0.76232965,  0.85819278,\n",
       "         0.95405591,  1.04991905,  1.14578218,  1.24164531,  1.33750845,\n",
       "         1.43337158,  1.52923471,  1.62509784,  1.72096098,  1.81682411,\n",
       "         1.91268724,  2.00855038,  2.10441351,  2.20027664,  2.29613978,\n",
       "         2.39200291]),\n",
       " [<matplotlib.patches.Polygon at 0x139b58040>])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_kde = ssms.support_utils.kde_class.LogKDE(out)\n",
    "my_kde_shifted_log = ssms.support_utils.kde_class.LogKDEshifted(out_log)\n",
    "my_kde_shifted = ssms.support_utils.kde_class.LogKDEshifted(out)\n",
    "\n",
    "sample_kde = my_kde.kde_sample(10000)\n",
    "sample_kde_shifted = my_kde_shifted.kde_sample(10000)\n",
    "sample_kde_shifted_log = my_kde_shifted_log.kde_sample(10000)\n",
    "plt.hist(\n",
    "    sample_kde[0] * sample_kde[1], bins=50, density=True, histtype=\"step\", color=\"blue\"\n",
    ")\n",
    "plt.hist(\n",
    "    sample_kde_shifted[\"rts\"] * sample_kde_shifted[\"choices\"],\n",
    "    bins=50,\n",
    "    density=True,\n",
    "    histtype=\"step\",\n",
    "    color=\"red\",\n",
    ")\n",
    "plt.hist(\n",
    "    sample_kde_shifted_log[\"rts\"] * sample_kde_shifted_log[\"choices\"],\n",
    "    bins=50,\n",
    "    density=True,\n",
    "    histtype=\"step\",\n",
    "    color=\"green\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.42254475],\n",
       "        [0.4172195 ],\n",
       "        [1.44519658],\n",
       "        ...,\n",
       "        [0.57311077],\n",
       "        [1.08196257],\n",
       "        [0.70934649]]),\n",
       " array([[-1.],\n",
       "        [-1.],\n",
       "        [-1.],\n",
       "        ...,\n",
       "        [ 1.],\n",
       "        [ 1.],\n",
       "        [ 1.]]),\n",
       " {'v': array([1.], dtype=float32),\n",
       "  'a': array([2.], dtype=float32),\n",
       "  'z': array([0.5], dtype=float32),\n",
       "  't': array([0.], dtype=float32),\n",
       "  's': 1.0,\n",
       "  'deadline': array([10.], dtype=float32),\n",
       "  'theta': array([0.7], dtype=float32),\n",
       "  'delta_t': 0.0010000000474974513,\n",
       "  'max_t': 20.0,\n",
       "  'n_samples': 10000,\n",
       "  'n_trials': 1,\n",
       "  'simulator': 'ddm_flexbound',\n",
       "  'boundary_fun_type': 'angle',\n",
       "  'possible_choices': [-1, 1],\n",
       "  'trajectory': array([[ 0.0000000e+00],\n",
       "         [-1.9366566e-02],\n",
       "         [ 3.6493689e-04],\n",
       "         ...,\n",
       "         [-9.9900000e+02],\n",
       "         [-9.9900000e+02],\n",
       "         [-9.9900000e+02]], dtype=float32),\n",
       "  'boundary': array([  2.       ,   1.9991577,   1.9983155, ..., -14.844082 ,\n",
       "         -14.844925 , -14.845766 ], dtype=float32),\n",
       "  'model': 'angle'})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(data_1[0], np.exp(evals_1), color=\"blue\")\n",
    "plt.plot(data_m1[0] * (-1), np.exp(evals_m1), color=\"blue\")\n",
    "plt.plot(data_1_shifted[0], np.exp(evals_1_shifted), color=\"red\")\n",
    "plt.plot(data_m1_shifted[0] * (-1), np.exp(evals_m1_shifted), color=\"red\")\n",
    "\n",
    "plt.hist(\n",
    "    out[\"rts\"][out[\"rts\"] != -999] * out[\"choices\"][out[\"rts\"] != -999],\n",
    "    bins=40,\n",
    "    histtype=\"step\",\n",
    "    density=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_kde = ssms.support_utils.kde_class.LogKDE(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_kde.kde_eval((data_1[\"rts\"], data_1[\"choices\"]))\n",
    "my_kde.kde_sample(n_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_kde_shifted.kde_sample(n_samples=10000)[\"rts\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_kde_shifted_log = ssms.support_utils.kde_class.LogKDEshifted(out_log)\n",
    "my_kde_shifted = ssms.support_utils.kde_class.LogKDEshifted(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1 = {\"rts\": np.linspace(0.01, 10, 1000), \"choices\": np.ones(1000)}\n",
    "data_m1 = {\"rts\": np.linspace(0.01, 10, 1000), \"choices\": (-1) * np.ones(1000)}\n",
    "\n",
    "data_l1 = {\"log_rts\": np.log(np.linspace(0.01, 10, 1000)), \"choices\": np.ones(1000)}\n",
    "data_lm1 = {\n",
    "    \"log_rts\": np.log(np.linspace(0.01, 10, 1000)),\n",
    "    \"choices\": (-1) * np.ones(1000),\n",
    "}\n",
    "\n",
    "# data_m1 = (np.linspace(0.01, 10, 1000), np.ones(1000) * (-1))\n",
    "\n",
    "# data_1_shifted = (np.linspace(0.01, 10, 1000), np.ones(1000))\n",
    "# data_m1_shifted = (np.linspace(0.01, 10, 1000), np.ones(1000) * (-1))\n",
    "\n",
    "# data_l1 = np.log(data_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'my_kde_shifted' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb Cell 15\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#Y145sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m evals_1 \u001b[39m=\u001b[39m my_kde_shifted\u001b[39m.\u001b[39mkde_eval(data_1)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#Y145sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m evals_m1 \u001b[39m=\u001b[39m my_kde_shifted\u001b[39m.\u001b[39mkde_eval(data_m1)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#Y145sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m evals_l1 \u001b[39m=\u001b[39m my_kde_shifted\u001b[39m.\u001b[39mkde_eval(data_1)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'my_kde_shifted' is not defined"
     ]
    }
   ],
   "source": [
    "evals_1 = my_kde_shifted.kde_eval(data_1)\n",
    "evals_m1 = my_kde_shifted.kde_eval(data_m1)\n",
    "\n",
    "evals_l1 = my_kde_shifted.kde_eval(data_1)\n",
    "print(\"this is the problem\")\n",
    "evals_lm1 = my_kde_shifted.kde_eval(data_lm1)\n",
    "\n",
    "# evals_1_shifted = my_kde_shifted.kde_eval(data_1_shifted)\n",
    "# evals_m1_shifted = my_kde_shifted.kde_eval(data_m1_shifted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first\n",
      "(1000,)\n",
      "second\n",
      "(1000,)\n",
      "first\n",
      "(1000,)\n",
      "second\n",
      "(1000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2bfdce560>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# my_kde_shifted.kde_eval(data_1)\n",
    "# my_kde_log_shifted.kde_eval(data_1)\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(data_1[\"rts\"], np.exp(my_kde_shifted.kde_eval(data_1)), color=\"blue\", label=\"\")\n",
    "plt.plot(\n",
    "    data_m1[\"rts\"] * (-1),\n",
    "    np.exp(my_kde_shifted.kde_eval(data_m1)),\n",
    "    color=\"blue\",\n",
    "    label=\"\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre\n",
      "(1000, 1)\n",
      "first\n",
      "(1000,)\n",
      "second\n",
      "(1000,)\n",
      "pre\n",
      "(1000, 1)\n",
      "first\n",
      "(1000,)\n",
      "second\n",
      "(1000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2bfe619f0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\n",
    "    np.exp(data_l1[\"log_rts\"]),\n",
    "    np.exp(\n",
    "        np.squeeze(my_kde_shifted.kde_eval(data_l1))\n",
    "        - np.log(np.exp(data_lm1[\"log_rts\"]) - t)\n",
    "    ),\n",
    "    color=\"blue\",\n",
    "    label=\"\",\n",
    ")\n",
    "plt.plot(\n",
    "    np.exp(data_lm1[\"log_rts\"]) * (-1),\n",
    "    np.exp(\n",
    "        np.squeeze(my_kde_shifted.kde_eval(data_lm1))\n",
    "        - np.log(np.exp(data_lm1[\"log_rts\"]) - t)\n",
    "    ),\n",
    "    color=\"blue\",\n",
    "    label=\"\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre\n",
      "(1000, 1)\n",
      "first\n",
      "(1000,)\n",
      "second\n",
      "(1000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1000,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.squeeze(np.exp(my_kde_shifted.kde_eval(data_l1))).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre\n",
      "(1000, 1)\n",
      "first\n",
      "(1000,)\n",
      "second\n",
      "(1000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_kde_shifted.kde_eval(data_l1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_2400/1136789726.py:1: RuntimeWarning: divide by zero encountered in log\n",
      "  np.log(np.exp(data_lm1['log_rts']) - t)\n",
      "/var/folders/gx/s43vynx550qbypcxm83fv56dzq4hgg/T/ipykernel_2400/1136789726.py:1: RuntimeWarning: invalid value encountered in log\n",
      "  np.log(np.exp(data_lm1['log_rts']) - t)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([        nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,         nan,\n",
       "               nan,         nan,         nan,         nan,        -inf,\n",
       "       -4.60517019, -3.91202301, -3.5065579 , -3.21887582, -2.99573227,\n",
       "       -2.81341072, -2.65926004, -2.52572864, -2.40794561, -2.30258509,\n",
       "       -2.20727491, -2.12026354, -2.04022083, -1.96611286, -1.89711998,\n",
       "       -1.83258146, -1.77195684, -1.71479843, -1.66073121, -1.60943791,\n",
       "       -1.56064775, -1.51412773, -1.46967597, -1.42711636, -1.38629436,\n",
       "       -1.34707365, -1.30933332, -1.27296568, -1.23787436, -1.2039728 ,\n",
       "       -1.17118298, -1.13943428, -1.10866262, -1.07880966, -1.04982212,\n",
       "       -1.02165125, -0.99425227, -0.96758403, -0.94160854, -0.91629073,\n",
       "       -0.89159812, -0.86750057, -0.84397007, -0.82098055, -0.7985077 ,\n",
       "       -0.77652879, -0.75502258, -0.73396918, -0.71334989, -0.69314718,\n",
       "       -0.67334455, -0.65392647, -0.63487827, -0.61618614, -0.597837  ,\n",
       "       -0.5798185 , -0.56211892, -0.54472718, -0.52763274, -0.51082562,\n",
       "       -0.49429632, -0.4780358 , -0.46203546, -0.4462871 , -0.43078292,\n",
       "       -0.41551544, -0.40047757, -0.38566248, -0.37106368, -0.35667494,\n",
       "       -0.34249031, -0.32850407, -0.31471074, -0.30110509, -0.28768207,\n",
       "       -0.27443685, -0.26136476, -0.24846136, -0.23572233, -0.22314355,\n",
       "       -0.21072103, -0.19845094, -0.18632958, -0.17435339, -0.16251893,\n",
       "       -0.15082289, -0.13926207, -0.12783337, -0.11653382, -0.10536052,\n",
       "       -0.09431068, -0.08338161, -0.07257069, -0.0618754 , -0.05129329,\n",
       "       -0.04082199, -0.03045921, -0.02020271, -0.01005034,  0.        ,\n",
       "        0.00995033,  0.01980263,  0.0295588 ,  0.03922071,  0.04879016,\n",
       "        0.05826891,  0.06765865,  0.07696104,  0.0861777 ,  0.09531018,\n",
       "        0.10436002,  0.11332869,  0.12221763,  0.13102826,  0.13976194,\n",
       "        0.14842001,  0.15700375,  0.16551444,  0.17395331,  0.18232156,\n",
       "        0.19062036,  0.19885086,  0.20701417,  0.21511138,  0.22314355,\n",
       "        0.23111172,  0.2390169 ,  0.24686008,  0.25464222,  0.26236426,\n",
       "        0.27002714,  0.27763174,  0.28517894,  0.29266961,  0.30010459,\n",
       "        0.3074847 ,  0.31481074,  0.3220835 ,  0.32930375,  0.33647224,\n",
       "        0.3435897 ,  0.35065687,  0.35767444,  0.36464311,  0.37156356,\n",
       "        0.37843644,  0.3852624 ,  0.39204209,  0.39877612,  0.40546511,\n",
       "        0.41210965,  0.41871033,  0.42526774,  0.43178242,  0.43825493,\n",
       "        0.44468582,  0.45107562,  0.45742485,  0.46373402,  0.47000363,\n",
       "        0.47623418,  0.48242615,  0.48858001,  0.49469624,  0.50077529,\n",
       "        0.5068176 ,  0.51282363,  0.51879379,  0.52472853,  0.53062825,\n",
       "        0.53649337,  0.54232429,  0.54812141,  0.55388511,  0.55961579,\n",
       "        0.56531381,  0.57097955,  0.57661336,  0.58221562,  0.58778666,\n",
       "        0.59332685,  0.5988365 ,  0.60431597,  0.60976557,  0.61518564,\n",
       "        0.62057649,  0.62593843,  0.63127178,  0.63657683,  0.64185389,\n",
       "        0.64710324,  0.65232519,  0.65752   ,  0.66268797,  0.66782937,\n",
       "        0.67294447,  0.67803354,  0.68309684,  0.68813464,  0.69314718,\n",
       "        0.69813472,  0.70309751,  0.70803579,  0.71294981,  0.71783979,\n",
       "        0.72270598,  0.72754861,  0.73236789,  0.73716407,  0.74193734,\n",
       "        0.74668795,  0.75141609,  0.75612198,  0.76080583,  0.76546784,\n",
       "        0.77010822,  0.77472717,  0.77932488,  0.78390154,  0.78845736,\n",
       "        0.79299252,  0.7975072 ,  0.80200159,  0.80647587,  0.81093022,\n",
       "        0.81536481,  0.81977983,  0.82417544,  0.82855182,  0.83290912,\n",
       "        0.83724752,  0.84156719,  0.84586827,  0.85015093,  0.85441533,\n",
       "        0.85866162,  0.86288996,  0.86710049,  0.87129337,  0.87546874,\n",
       "        0.87962675,  0.88376754,  0.88789126,  0.89199804,  0.89608802,\n",
       "        0.90016135,  0.90421815,  0.90825856,  0.91228271,  0.91629073,\n",
       "        0.92028275,  0.9242589 ,  0.9282193 ,  0.93216408,  0.93609336,\n",
       "        0.94000726,  0.9439059 ,  0.9477894 ,  0.95165788,  0.95551145,\n",
       "        0.95935022,  0.96317432,  0.96698385,  0.97077892,  0.97455964,\n",
       "        0.97832612,  0.98207847,  0.98581679,  0.98954119,  0.99325177,\n",
       "        0.99694863,  1.00063188,  1.00430161,  1.00795792,  1.01160091,\n",
       "        1.01523068,  1.01884732,  1.02245093,  1.0260416 ,  1.02961942,\n",
       "        1.03318448,  1.03673688,  1.04027671,  1.04380405,  1.04731899,\n",
       "        1.05082162,  1.05431203,  1.05779029,  1.0612565 ,  1.06471074,\n",
       "        1.06815308,  1.07158362,  1.07500242,  1.07840958,  1.08180517,\n",
       "        1.08518927,  1.08856195,  1.0919233 ,  1.09527339,  1.09861229,\n",
       "        1.10194008,  1.10525683,  1.10856262,  1.11185752,  1.11514159,\n",
       "        1.11841492,  1.12167756,  1.1249296 ,  1.12817109,  1.13140211,\n",
       "        1.13462273,  1.137833  ,  1.141033  ,  1.1442228 ,  1.14740245,\n",
       "        1.15057203,  1.15373159,  1.1568812 ,  1.16002092,  1.16315081,\n",
       "        1.16627094,  1.16938136,  1.17248214,  1.17557333,  1.178655  ,\n",
       "        1.1817272 ,  1.18478998,  1.18784342,  1.19088756,  1.19392247,\n",
       "        1.19694819,  1.19996478,  1.2029723 ,  1.20597081,  1.20896035,\n",
       "        1.21194097,  1.21491274,  1.21787571,  1.22082992,  1.22377543,\n",
       "        1.22671229,  1.22964055,  1.23256026,  1.23547147,  1.23837423,\n",
       "        1.24126859,  1.24415459,  1.24703229,  1.24990174,  1.25276297,\n",
       "        1.25561604,  1.25846099,  1.26129787,  1.26412673,  1.2669476 ,\n",
       "        1.26976054,  1.2725656 ,  1.2753628 ,  1.2781522 ,  1.28093385,\n",
       "        1.28370777,  1.28647403,  1.28923265,  1.29198368,  1.29472717,\n",
       "        1.29746315,  1.30019166,  1.30291275,  1.30562646,  1.30833282,\n",
       "        1.31103188,  1.31372367,  1.31640823,  1.31908561,  1.32175584,\n",
       "        1.32441896,  1.327075  ,  1.32972401,  1.33236602,  1.33500107,\n",
       "        1.33762919,  1.34025042,  1.3428648 ,  1.34547237,  1.34807315,\n",
       "        1.35066718,  1.35325451,  1.35583515,  1.35840916,  1.36097655,\n",
       "        1.36353737,  1.36609165,  1.36863943,  1.37118072,  1.37371558,\n",
       "        1.37624403,  1.37876609,  1.38128182,  1.38379123,  1.38629436,\n",
       "        1.38879124,  1.3912819 ,  1.39376638,  1.39624469,  1.39871688,\n",
       "        1.40118297,  1.403643  ,  1.40609699,  1.40854497,  1.41098697,\n",
       "        1.41342303,  1.41585316,  1.41827741,  1.42069579,  1.42310833,\n",
       "        1.42551507,  1.42791604,  1.43031125,  1.43270073,  1.43508453,\n",
       "        1.43746265,  1.43983513,  1.44220199,  1.44456327,  1.44691898,\n",
       "        1.44926916,  1.45161383,  1.45395301,  1.45628673,  1.45861502,\n",
       "        1.4609379 ,  1.4632554 ,  1.46556754,  1.46787435,  1.47017585,\n",
       "        1.47247206,  1.47476301,  1.47704872,  1.47932923,  1.48160454,\n",
       "        1.48387469,  1.4861397 ,  1.48839958,  1.49065438,  1.4929041 ,\n",
       "        1.49514877,  1.49738841,  1.49962305,  1.5018527 ,  1.5040774 ,\n",
       "        1.50629715,  1.50851199,  1.51072194,  1.51292701,  1.51512723,\n",
       "        1.51732262,  1.5195132 ,  1.521699  ,  1.52388002,  1.5260563 ,\n",
       "        1.52822786,  1.53039471,  1.53255687,  1.53471437,  1.53686722,\n",
       "        1.53901545,  1.54115907,  1.54329811,  1.54543258,  1.54756251,\n",
       "        1.54968791,  1.5518088 ,  1.5539252 ,  1.55603714,  1.55814462,\n",
       "        1.56024767,  1.5623463 ,  1.56444055,  1.56653041,  1.56861592,\n",
       "        1.57069708,  1.57277393,  1.57484647,  1.57691472,  1.5789787 ,\n",
       "        1.58103844,  1.58309394,  1.58514522,  1.5871923 ,  1.58923521,\n",
       "        1.59127394,  1.59330853,  1.59533899,  1.59736533,  1.59938758,\n",
       "        1.60140574,  1.60341984,  1.60542989,  1.60743591,  1.60943791,\n",
       "        1.61143592,  1.61342993,  1.61541998,  1.61740608,  1.61938824,\n",
       "        1.62136648,  1.62334082,  1.62531126,  1.62727783,  1.62924054,\n",
       "        1.6311994 ,  1.63315444,  1.63510566,  1.63705308,  1.63899671,\n",
       "        1.64093658,  1.64287269,  1.64480506,  1.6467337 ,  1.64865863,\n",
       "        1.65057986,  1.6524974 ,  1.65441128,  1.6563215 ,  1.65822808,\n",
       "        1.66013103,  1.66203036,  1.6639261 ,  1.66581825,  1.66770682,\n",
       "        1.66959184,  1.6714733 ,  1.67335124,  1.67522565,  1.67709656,\n",
       "        1.67896398,  1.68082791,  1.68268837,  1.68454538,  1.68639895,\n",
       "        1.68824909,  1.69009582,  1.69193913,  1.69377906,  1.69561561,\n",
       "        1.69744879,  1.69927862,  1.7011051 ,  1.70292826,  1.70474809,\n",
       "        1.70656462,  1.70837786,  1.71018782,  1.7119945 ,  1.71379793,\n",
       "        1.71559811,  1.71739505,  1.71918878,  1.72097929,  1.7227666 ,\n",
       "        1.72455072,  1.72633166,  1.72810944,  1.72988407,  1.73165555,\n",
       "        1.73342389,  1.73518912,  1.73695123,  1.73871025,  1.74046617,\n",
       "        1.74221902,  1.74396881,  1.74571553,  1.74745921,  1.74919985,\n",
       "        1.75093747,  1.75267208,  1.75440368,  1.75613229,  1.75785792,\n",
       "        1.75958057,  1.76130026,  1.763017  ,  1.7647308 ,  1.76644166,\n",
       "        1.7681496 ,  1.76985463,  1.77155676,  1.773256  ,  1.77495235,\n",
       "        1.77664583,  1.77833645,  1.78002421,  1.78170913,  1.78339122,\n",
       "        1.78507048,  1.78674693,  1.78842057,  1.79009141,  1.79175947,\n",
       "        1.79342475,  1.79508726,  1.79674701,  1.79840401,  1.80005827,\n",
       "        1.8017098 ,  1.80335861,  1.8050047 ,  1.80664808,  1.80828877,\n",
       "        1.80992677,  1.8115621 ,  1.81319475,  1.81482474,  1.81645208,\n",
       "        1.81807678,  1.81969884,  1.82131827,  1.82293509,  1.82454929,\n",
       "        1.8261609 ,  1.82776991,  1.82937633,  1.83098018,  1.83258146,\n",
       "        1.83418019,  1.83577635,  1.83736998,  1.83896107,  1.84054963,\n",
       "        1.84213568,  1.84371921,  1.84530024,  1.84687877,  1.84845481,\n",
       "        1.85002838,  1.85159947,  1.8531681 ,  1.85473427,  1.85629799,\n",
       "        1.85785927,  1.85941812,  1.86097454,  1.86252854,  1.86408013,\n",
       "        1.86562932,  1.86717611,  1.86872051,  1.87026253,  1.87180218,\n",
       "        1.87333946,  1.87487438,  1.87640694,  1.87793717,  1.87946505,\n",
       "        1.8809906 ,  1.88251383,  1.88403475,  1.88555335,  1.88706965,\n",
       "        1.88858365,  1.89009537,  1.8916048 ,  1.89311196,  1.89461685,\n",
       "        1.89611948,  1.89761986,  1.89911799,  1.90061387,  1.90210753,\n",
       "        1.90359895,  1.90508815,  1.90657514,  1.90805992,  1.9095425 ,\n",
       "        1.91102289,  1.91250109,  1.9139771 ,  1.91545094,  1.91692261,\n",
       "        1.91839212,  1.91985947,  1.92132467,  1.92278773,  1.92424865,\n",
       "        1.92570744,  1.92716411,  1.92861865,  1.93007109,  1.93152141,\n",
       "        1.93296964,  1.93441577,  1.93585981,  1.93730177,  1.93874166,\n",
       "        1.94017947,  1.94161522,  1.94304892,  1.94448056,  1.94591015,\n",
       "        1.9473377 ,  1.94876322,  1.95018671,  1.95160817,  1.95302762,\n",
       "        1.95444505,  1.95586048,  1.95727391,  1.95868534,  1.96009478,\n",
       "        1.96150224,  1.96290773,  1.96431123,  1.96571278,  1.96711236,\n",
       "        1.96850998,  1.96990565,  1.97129938,  1.97269117,  1.97408103,\n",
       "        1.97546895,  1.97685495,  1.97823904,  1.97962121,  1.98100147,\n",
       "        1.98237983,  1.98375629,  1.98513086,  1.98650355,  1.98787435,\n",
       "        1.98924327,  1.99061033,  1.99197552,  1.99333884,  1.99470031,\n",
       "        1.99605993,  1.99741771,  1.99877364,  2.00012773,  2.00148   ,\n",
       "        2.00283044,  2.00417906,  2.00552586,  2.00687085,  2.00821403,\n",
       "        2.00955541,  2.010895  ,  2.01223279,  2.0135688 ,  2.01490302,\n",
       "        2.01623547,  2.01756614,  2.01889504,  2.02022218,  2.02154756,\n",
       "        2.02287119,  2.02419307,  2.0255132 ,  2.02683159,  2.02814825,\n",
       "        2.02946317,  2.03077637,  2.03208785,  2.0333976 ,  2.03470565,\n",
       "        2.03601198,  2.03731662,  2.03861955,  2.03992078,  2.04122033,\n",
       "        2.04251819,  2.04381436,  2.04510886,  2.04640169,  2.04769284,\n",
       "        2.04898233,  2.05027016,  2.05155634,  2.05284086,  2.05412373,\n",
       "        2.05540496,  2.05668455,  2.05796251,  2.05923883,  2.06051353,\n",
       "        2.06178661,  2.06305806,  2.0643279 ,  2.06559613,  2.06686276,\n",
       "        2.06812778,  2.06939121,  2.07065304,  2.07191328,  2.07317193,\n",
       "        2.074429  ,  2.07568449,  2.07693841,  2.07819076,  2.07944154,\n",
       "        2.08069076,  2.08193842,  2.08318453,  2.08442908,  2.08567209,\n",
       "        2.08691356,  2.08815348,  2.08939187,  2.09062873,  2.09186406,\n",
       "        2.09309787,  2.09433015,  2.09556092,  2.09679018,  2.09801793,\n",
       "        2.09924417,  2.10046891,  2.10169215,  2.1029139 ,  2.10413415,\n",
       "        2.10535292,  2.10657021,  2.10778601,  2.10900034,  2.1102132 ,\n",
       "        2.11142459,  2.11263451,  2.11384297,  2.11504997,  2.11625551,\n",
       "        2.11745961,  2.11866225,  2.11986346,  2.12106322,  2.12226154,\n",
       "        2.12345843,  2.12465388,  2.12584791,  2.12704052,  2.12823171,\n",
       "        2.12942147,  2.13060983,  2.13179677,  2.13298231,  2.13416644,\n",
       "        2.13534917,  2.13653051,  2.13771045,  2.138889  ,  2.14006616,\n",
       "        2.14124194,  2.14241634,  2.14358936,  2.14476101,  2.14593128,\n",
       "        2.14710019,  2.14826773,  2.14943391,  2.15059874,  2.1517622 ,\n",
       "        2.15292432,  2.15408508,  2.15524451,  2.15640258,  2.15755932,\n",
       "        2.15871472,  2.15986879,  2.16102153,  2.16217294,  2.16332303,\n",
       "        2.16447179,  2.16561924,  2.16676537,  2.16791019,  2.1690537 ,\n",
       "        2.1701959 ,  2.17133681,  2.17247641,  2.17361471,  2.17475172,\n",
       "        2.17588744,  2.17702187,  2.17815501,  2.17928688,  2.18041746,\n",
       "        2.18154676,  2.1826748 ,  2.18380156,  2.18492705,  2.18605128,\n",
       "        2.18717424,  2.18829595,  2.18941639,  2.19053559,  2.19165353,\n",
       "        2.19277023,  2.19388568,  2.19499988,  2.19611285,  2.19722458])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(np.exp(data_lm1[\"log_rts\"]) - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.01,  0.02,  0.03,  0.04,  0.05,  0.06,  0.07,  0.08,  0.09,\n",
       "        0.1 ,  0.11,  0.12,  0.13,  0.14,  0.15,  0.16,  0.17,  0.18,\n",
       "        0.19,  0.2 ,  0.21,  0.22,  0.23,  0.24,  0.25,  0.26,  0.27,\n",
       "        0.28,  0.29,  0.3 ,  0.31,  0.32,  0.33,  0.34,  0.35,  0.36,\n",
       "        0.37,  0.38,  0.39,  0.4 ,  0.41,  0.42,  0.43,  0.44,  0.45,\n",
       "        0.46,  0.47,  0.48,  0.49,  0.5 ,  0.51,  0.52,  0.53,  0.54,\n",
       "        0.55,  0.56,  0.57,  0.58,  0.59,  0.6 ,  0.61,  0.62,  0.63,\n",
       "        0.64,  0.65,  0.66,  0.67,  0.68,  0.69,  0.7 ,  0.71,  0.72,\n",
       "        0.73,  0.74,  0.75,  0.76,  0.77,  0.78,  0.79,  0.8 ,  0.81,\n",
       "        0.82,  0.83,  0.84,  0.85,  0.86,  0.87,  0.88,  0.89,  0.9 ,\n",
       "        0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99,\n",
       "        1.  ,  1.01,  1.02,  1.03,  1.04,  1.05,  1.06,  1.07,  1.08,\n",
       "        1.09,  1.1 ,  1.11,  1.12,  1.13,  1.14,  1.15,  1.16,  1.17,\n",
       "        1.18,  1.19,  1.2 ,  1.21,  1.22,  1.23,  1.24,  1.25,  1.26,\n",
       "        1.27,  1.28,  1.29,  1.3 ,  1.31,  1.32,  1.33,  1.34,  1.35,\n",
       "        1.36,  1.37,  1.38,  1.39,  1.4 ,  1.41,  1.42,  1.43,  1.44,\n",
       "        1.45,  1.46,  1.47,  1.48,  1.49,  1.5 ,  1.51,  1.52,  1.53,\n",
       "        1.54,  1.55,  1.56,  1.57,  1.58,  1.59,  1.6 ,  1.61,  1.62,\n",
       "        1.63,  1.64,  1.65,  1.66,  1.67,  1.68,  1.69,  1.7 ,  1.71,\n",
       "        1.72,  1.73,  1.74,  1.75,  1.76,  1.77,  1.78,  1.79,  1.8 ,\n",
       "        1.81,  1.82,  1.83,  1.84,  1.85,  1.86,  1.87,  1.88,  1.89,\n",
       "        1.9 ,  1.91,  1.92,  1.93,  1.94,  1.95,  1.96,  1.97,  1.98,\n",
       "        1.99,  2.  ,  2.01,  2.02,  2.03,  2.04,  2.05,  2.06,  2.07,\n",
       "        2.08,  2.09,  2.1 ,  2.11,  2.12,  2.13,  2.14,  2.15,  2.16,\n",
       "        2.17,  2.18,  2.19,  2.2 ,  2.21,  2.22,  2.23,  2.24,  2.25,\n",
       "        2.26,  2.27,  2.28,  2.29,  2.3 ,  2.31,  2.32,  2.33,  2.34,\n",
       "        2.35,  2.36,  2.37,  2.38,  2.39,  2.4 ,  2.41,  2.42,  2.43,\n",
       "        2.44,  2.45,  2.46,  2.47,  2.48,  2.49,  2.5 ,  2.51,  2.52,\n",
       "        2.53,  2.54,  2.55,  2.56,  2.57,  2.58,  2.59,  2.6 ,  2.61,\n",
       "        2.62,  2.63,  2.64,  2.65,  2.66,  2.67,  2.68,  2.69,  2.7 ,\n",
       "        2.71,  2.72,  2.73,  2.74,  2.75,  2.76,  2.77,  2.78,  2.79,\n",
       "        2.8 ,  2.81,  2.82,  2.83,  2.84,  2.85,  2.86,  2.87,  2.88,\n",
       "        2.89,  2.9 ,  2.91,  2.92,  2.93,  2.94,  2.95,  2.96,  2.97,\n",
       "        2.98,  2.99,  3.  ,  3.01,  3.02,  3.03,  3.04,  3.05,  3.06,\n",
       "        3.07,  3.08,  3.09,  3.1 ,  3.11,  3.12,  3.13,  3.14,  3.15,\n",
       "        3.16,  3.17,  3.18,  3.19,  3.2 ,  3.21,  3.22,  3.23,  3.24,\n",
       "        3.25,  3.26,  3.27,  3.28,  3.29,  3.3 ,  3.31,  3.32,  3.33,\n",
       "        3.34,  3.35,  3.36,  3.37,  3.38,  3.39,  3.4 ,  3.41,  3.42,\n",
       "        3.43,  3.44,  3.45,  3.46,  3.47,  3.48,  3.49,  3.5 ,  3.51,\n",
       "        3.52,  3.53,  3.54,  3.55,  3.56,  3.57,  3.58,  3.59,  3.6 ,\n",
       "        3.61,  3.62,  3.63,  3.64,  3.65,  3.66,  3.67,  3.68,  3.69,\n",
       "        3.7 ,  3.71,  3.72,  3.73,  3.74,  3.75,  3.76,  3.77,  3.78,\n",
       "        3.79,  3.8 ,  3.81,  3.82,  3.83,  3.84,  3.85,  3.86,  3.87,\n",
       "        3.88,  3.89,  3.9 ,  3.91,  3.92,  3.93,  3.94,  3.95,  3.96,\n",
       "        3.97,  3.98,  3.99,  4.  ,  4.01,  4.02,  4.03,  4.04,  4.05,\n",
       "        4.06,  4.07,  4.08,  4.09,  4.1 ,  4.11,  4.12,  4.13,  4.14,\n",
       "        4.15,  4.16,  4.17,  4.18,  4.19,  4.2 ,  4.21,  4.22,  4.23,\n",
       "        4.24,  4.25,  4.26,  4.27,  4.28,  4.29,  4.3 ,  4.31,  4.32,\n",
       "        4.33,  4.34,  4.35,  4.36,  4.37,  4.38,  4.39,  4.4 ,  4.41,\n",
       "        4.42,  4.43,  4.44,  4.45,  4.46,  4.47,  4.48,  4.49,  4.5 ,\n",
       "        4.51,  4.52,  4.53,  4.54,  4.55,  4.56,  4.57,  4.58,  4.59,\n",
       "        4.6 ,  4.61,  4.62,  4.63,  4.64,  4.65,  4.66,  4.67,  4.68,\n",
       "        4.69,  4.7 ,  4.71,  4.72,  4.73,  4.74,  4.75,  4.76,  4.77,\n",
       "        4.78,  4.79,  4.8 ,  4.81,  4.82,  4.83,  4.84,  4.85,  4.86,\n",
       "        4.87,  4.88,  4.89,  4.9 ,  4.91,  4.92,  4.93,  4.94,  4.95,\n",
       "        4.96,  4.97,  4.98,  4.99,  5.  ,  5.01,  5.02,  5.03,  5.04,\n",
       "        5.05,  5.06,  5.07,  5.08,  5.09,  5.1 ,  5.11,  5.12,  5.13,\n",
       "        5.14,  5.15,  5.16,  5.17,  5.18,  5.19,  5.2 ,  5.21,  5.22,\n",
       "        5.23,  5.24,  5.25,  5.26,  5.27,  5.28,  5.29,  5.3 ,  5.31,\n",
       "        5.32,  5.33,  5.34,  5.35,  5.36,  5.37,  5.38,  5.39,  5.4 ,\n",
       "        5.41,  5.42,  5.43,  5.44,  5.45,  5.46,  5.47,  5.48,  5.49,\n",
       "        5.5 ,  5.51,  5.52,  5.53,  5.54,  5.55,  5.56,  5.57,  5.58,\n",
       "        5.59,  5.6 ,  5.61,  5.62,  5.63,  5.64,  5.65,  5.66,  5.67,\n",
       "        5.68,  5.69,  5.7 ,  5.71,  5.72,  5.73,  5.74,  5.75,  5.76,\n",
       "        5.77,  5.78,  5.79,  5.8 ,  5.81,  5.82,  5.83,  5.84,  5.85,\n",
       "        5.86,  5.87,  5.88,  5.89,  5.9 ,  5.91,  5.92,  5.93,  5.94,\n",
       "        5.95,  5.96,  5.97,  5.98,  5.99,  6.  ,  6.01,  6.02,  6.03,\n",
       "        6.04,  6.05,  6.06,  6.07,  6.08,  6.09,  6.1 ,  6.11,  6.12,\n",
       "        6.13,  6.14,  6.15,  6.16,  6.17,  6.18,  6.19,  6.2 ,  6.21,\n",
       "        6.22,  6.23,  6.24,  6.25,  6.26,  6.27,  6.28,  6.29,  6.3 ,\n",
       "        6.31,  6.32,  6.33,  6.34,  6.35,  6.36,  6.37,  6.38,  6.39,\n",
       "        6.4 ,  6.41,  6.42,  6.43,  6.44,  6.45,  6.46,  6.47,  6.48,\n",
       "        6.49,  6.5 ,  6.51,  6.52,  6.53,  6.54,  6.55,  6.56,  6.57,\n",
       "        6.58,  6.59,  6.6 ,  6.61,  6.62,  6.63,  6.64,  6.65,  6.66,\n",
       "        6.67,  6.68,  6.69,  6.7 ,  6.71,  6.72,  6.73,  6.74,  6.75,\n",
       "        6.76,  6.77,  6.78,  6.79,  6.8 ,  6.81,  6.82,  6.83,  6.84,\n",
       "        6.85,  6.86,  6.87,  6.88,  6.89,  6.9 ,  6.91,  6.92,  6.93,\n",
       "        6.94,  6.95,  6.96,  6.97,  6.98,  6.99,  7.  ,  7.01,  7.02,\n",
       "        7.03,  7.04,  7.05,  7.06,  7.07,  7.08,  7.09,  7.1 ,  7.11,\n",
       "        7.12,  7.13,  7.14,  7.15,  7.16,  7.17,  7.18,  7.19,  7.2 ,\n",
       "        7.21,  7.22,  7.23,  7.24,  7.25,  7.26,  7.27,  7.28,  7.29,\n",
       "        7.3 ,  7.31,  7.32,  7.33,  7.34,  7.35,  7.36,  7.37,  7.38,\n",
       "        7.39,  7.4 ,  7.41,  7.42,  7.43,  7.44,  7.45,  7.46,  7.47,\n",
       "        7.48,  7.49,  7.5 ,  7.51,  7.52,  7.53,  7.54,  7.55,  7.56,\n",
       "        7.57,  7.58,  7.59,  7.6 ,  7.61,  7.62,  7.63,  7.64,  7.65,\n",
       "        7.66,  7.67,  7.68,  7.69,  7.7 ,  7.71,  7.72,  7.73,  7.74,\n",
       "        7.75,  7.76,  7.77,  7.78,  7.79,  7.8 ,  7.81,  7.82,  7.83,\n",
       "        7.84,  7.85,  7.86,  7.87,  7.88,  7.89,  7.9 ,  7.91,  7.92,\n",
       "        7.93,  7.94,  7.95,  7.96,  7.97,  7.98,  7.99,  8.  ,  8.01,\n",
       "        8.02,  8.03,  8.04,  8.05,  8.06,  8.07,  8.08,  8.09,  8.1 ,\n",
       "        8.11,  8.12,  8.13,  8.14,  8.15,  8.16,  8.17,  8.18,  8.19,\n",
       "        8.2 ,  8.21,  8.22,  8.23,  8.24,  8.25,  8.26,  8.27,  8.28,\n",
       "        8.29,  8.3 ,  8.31,  8.32,  8.33,  8.34,  8.35,  8.36,  8.37,\n",
       "        8.38,  8.39,  8.4 ,  8.41,  8.42,  8.43,  8.44,  8.45,  8.46,\n",
       "        8.47,  8.48,  8.49,  8.5 ,  8.51,  8.52,  8.53,  8.54,  8.55,\n",
       "        8.56,  8.57,  8.58,  8.59,  8.6 ,  8.61,  8.62,  8.63,  8.64,\n",
       "        8.65,  8.66,  8.67,  8.68,  8.69,  8.7 ,  8.71,  8.72,  8.73,\n",
       "        8.74,  8.75,  8.76,  8.77,  8.78,  8.79,  8.8 ,  8.81,  8.82,\n",
       "        8.83,  8.84,  8.85,  8.86,  8.87,  8.88,  8.89,  8.9 ,  8.91,\n",
       "        8.92,  8.93,  8.94,  8.95,  8.96,  8.97,  8.98,  8.99,  9.  ,\n",
       "        9.01,  9.02,  9.03,  9.04,  9.05,  9.06,  9.07,  9.08,  9.09,\n",
       "        9.1 ,  9.11,  9.12,  9.13,  9.14,  9.15,  9.16,  9.17,  9.18,\n",
       "        9.19,  9.2 ,  9.21,  9.22,  9.23,  9.24,  9.25,  9.26,  9.27,\n",
       "        9.28,  9.29,  9.3 ,  9.31,  9.32,  9.33,  9.34,  9.35,  9.36,\n",
       "        9.37,  9.38,  9.39,  9.4 ,  9.41,  9.42,  9.43,  9.44,  9.45,\n",
       "        9.46,  9.47,  9.48,  9.49,  9.5 ,  9.51,  9.52,  9.53,  9.54,\n",
       "        9.55,  9.56,  9.57,  9.58,  9.59,  9.6 ,  9.61,  9.62,  9.63,\n",
       "        9.64,  9.65,  9.66,  9.67,  9.68,  9.69,  9.7 ,  9.71,  9.72,\n",
       "        9.73,  9.74,  9.75,  9.76,  9.77,  9.78,  9.79,  9.8 ,  9.81,\n",
       "        9.82,  9.83,  9.84,  9.85,  9.86,  9.87,  9.88,  9.89,  9.9 ,\n",
       "        9.91,  9.92,  9.93,  9.94,  9.95,  9.96,  9.97,  9.98,  9.99,\n",
       "       10.  ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(data_lm1[\"log_rts\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_l1 = np.log(data_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'keys'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb Cell 14\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#X61sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m evals_1 \u001b[39m=\u001b[39m my_kde_shifted\u001b[39m.\u001b[39;49mkde_eval(data_1)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#X61sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m evals_m1 \u001b[39m=\u001b[39m my_kde_shifted\u001b[39m.\u001b[39mkde_eval(data_m1)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#X61sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m evals_1_shifted \u001b[39m=\u001b[39m my_kde_shifted\u001b[39m.\u001b[39mkde_eval(data_1_shifted)\n",
      "File \u001b[0;32m~/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/ssms/support_utils/kde_class.py:430\u001b[0m, in \u001b[0;36mLogKDEshifted.kde_eval\u001b[0;34m(self, data, log_eval, lb, eps)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mkde_eval\u001b[39m(\u001b[39mself\u001b[39m, data \u001b[39m=\u001b[39m {}, log_eval \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m, lb\u001b[39m=\u001b[39m\u001b[39m-\u001b[39m\u001b[39m66.774\u001b[39m, eps\u001b[39m=\u001b[39m\u001b[39m10e-5\u001b[39m):\n\u001b[0;32m--> 430\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mlog_rts\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m data\u001b[39m.\u001b[39;49mkeys() \u001b[39mand\u001b[39;00m  (\u001b[39m'\u001b[39m\u001b[39mrts\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m data\u001b[39m.\u001b[39mkeys()):\n\u001b[1;32m    431\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_kde_eval_log_rt(data\u001b[39m=\u001b[39mdata, log_eval\u001b[39m=\u001b[39mlog_eval, lb\u001b[39m=\u001b[39mlb, eps\u001b[39m=\u001b[39meps)\n\u001b[1;32m    432\u001b[0m     \u001b[39melif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39mrts\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m data\u001b[39m.\u001b[39mkeys() \u001b[39mand\u001b[39;00m (\u001b[39m'\u001b[39m\u001b[39mlog_rts\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m data\u001b[39m.\u001b[39mkeys()):\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'keys'"
     ]
    }
   ],
   "source": [
    "evals_1 = my_kde_shifted.kde_eval(data_1)\n",
    "evals_m1 = my_kde_shifted.kde_eval(data_m1)\n",
    "\n",
    "evals_1_shifted = my_kde_shifted.kde_eval(data_1_shifted)\n",
    "evals_m1_shifted = my_kde_shifted.kde_eval(data_m1_shifted)\n",
    "\n",
    "evals_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.79595485e-01, 9.97337554e-02, 9.20619281e-02, 9.46192038e-02,\n",
       "        7.84231239e-02, 4.77358145e-02, 3.15397346e-02, 3.23921599e-02,\n",
       "        1.53436547e-02, 5.96697682e-03, 8.52425260e-04, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 1.36388042e-02, 2.05434488e-01, 5.48961867e-01,\n",
       "        8.15770974e-01, 1.01012393e+00, 1.00756666e+00, 8.80555293e-01,\n",
       "        7.97017618e-01, 6.64891703e-01, 4.92701800e-01, 7.85083664e-01]),\n",
       " array([-2.49999499e+00, -2.37499517e+00, -2.24999534e+00, -2.12499551e+00,\n",
       "        -1.99999568e+00, -1.87499586e+00, -1.74999603e+00, -1.62499620e+00,\n",
       "        -1.49999638e+00, -1.37499655e+00, -1.24999672e+00, -1.12499689e+00,\n",
       "        -9.99997067e-01, -8.74997240e-01, -7.49997413e-01, -6.24997586e-01,\n",
       "        -4.99997759e-01, -3.74997932e-01, -2.49998105e-01, -1.24998277e-01,\n",
       "         1.54972076e-06,  1.25001377e-01,  2.50001204e-01,  3.75001031e-01,\n",
       "         5.00000858e-01,  6.25000685e-01,  7.50000513e-01,  8.75000340e-01,\n",
       "         1.00000017e+00,  1.12499999e+00,  1.24999982e+00,  1.37499965e+00,\n",
       "         1.49999948e+00,  1.62499930e+00,  1.74999913e+00,  1.87499896e+00,\n",
       "         1.99999878e+00,  2.12499861e+00,  2.24999844e+00,  2.37499827e+00,\n",
       "         2.49999809e+00]),\n",
       " [<matplotlib.patches.Polygon at 0x148624af0>])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(data_1[0], np.exp(evals_1), color=\"blue\")\n",
    "plt.plot(data_m1[0] * (-1), np.exp(evals_m1), color=\"blue\")\n",
    "plt.plot(data_1_shifted[0], np.exp(evals_1_shifted), color=\"red\")\n",
    "plt.plot(data_m1_shifted[0] * (-1), np.exp(evals_m1_shifted), color=\"red\")\n",
    "\n",
    "plt.hist(\n",
    "    out[\"rts\"][out[\"rts\"] != -999] * out[\"choices\"][out[\"rts\"] != -999],\n",
    "    bins=40,\n",
    "    histtype=\"step\",\n",
    "    density=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'my_kde_shifted' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb Cell 25\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#Y130sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m out_kde_shifted \u001b[39m=\u001b[39m my_kde_shifted\u001b[39m.\u001b[39mkde_sample(n_samples \u001b[39m=\u001b[39m \u001b[39m10000\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/notebooks/test_deadline.ipynb#Y130sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m out_kde \u001b[39m=\u001b[39m my_kde\u001b[39m.\u001b[39mkde_sample(n_samples \u001b[39m=\u001b[39m \u001b[39m10000\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'my_kde_shifted' is not defined"
     ]
    }
   ],
   "source": [
    "out_kde_shifted = my_kde_shifted.kde_sample(n_samples=10000)\n",
    "out_kde = my_kde.kde_sample(n_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.],\n",
       "       [-1.],\n",
       "       [-1.],\n",
       "       ...,\n",
       "       [ 1.],\n",
       "       [ 1.],\n",
       "       [ 1.]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_kde[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.79595485e-01, 9.97337554e-02, 9.20619281e-02, 9.46192038e-02,\n",
       "        7.84231239e-02, 4.77358145e-02, 3.15397346e-02, 3.23921599e-02,\n",
       "        1.53436547e-02, 5.96697682e-03, 8.52425260e-04, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 1.36388042e-02, 2.05434488e-01, 5.48961867e-01,\n",
       "        8.15770974e-01, 1.01012393e+00, 1.00756666e+00, 8.80555293e-01,\n",
       "        7.97017618e-01, 6.64891703e-01, 4.92701800e-01, 7.85083664e-01]),\n",
       " array([-2.49999499e+00, -2.37499517e+00, -2.24999534e+00, -2.12499551e+00,\n",
       "        -1.99999568e+00, -1.87499586e+00, -1.74999603e+00, -1.62499620e+00,\n",
       "        -1.49999638e+00, -1.37499655e+00, -1.24999672e+00, -1.12499689e+00,\n",
       "        -9.99997067e-01, -8.74997240e-01, -7.49997413e-01, -6.24997586e-01,\n",
       "        -4.99997759e-01, -3.74997932e-01, -2.49998105e-01, -1.24998277e-01,\n",
       "         1.54972076e-06,  1.25001377e-01,  2.50001204e-01,  3.75001031e-01,\n",
       "         5.00000858e-01,  6.25000685e-01,  7.50000513e-01,  8.75000340e-01,\n",
       "         1.00000017e+00,  1.12499999e+00,  1.24999982e+00,  1.37499965e+00,\n",
       "         1.49999948e+00,  1.62499930e+00,  1.74999913e+00,  1.87499896e+00,\n",
       "         1.99999878e+00,  2.12499861e+00,  2.24999844e+00,  2.37499827e+00,\n",
       "         2.49999809e+00]),\n",
       " [<matplotlib.patches.Polygon at 0x148e02ef0>])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# plt.plot(data_1[0], np.exp(evals_1), color=\"blue\")\n",
    "# plt.plot(data_m1[0] * (-1), np.exp(evals_m1), color=\"blue\")\n",
    "# plt.plot(data_1_shifted[0], np.exp(evals_1_shifted), color=\"red\")\n",
    "# plt.plot(data_m1_shifted[0] * (-1), np.exp(evals_m1_shifted), color=\"red\")\n",
    "plt.hist(\n",
    "    out_kde_shifted[0] * out_kde_shifted[1], bins=100, histtype=\"step\", density=True\n",
    ")\n",
    "plt.hist(out_kde[0] * out_kde[1], bins=100, histtype=\"step\", density=True)\n",
    "\n",
    "\n",
    "plt.hist(\n",
    "    out[\"rts\"][out[\"rts\"] != -999] * out[\"choices\"][out[\"rts\"] != -999],\n",
    "    bins=40,\n",
    "    histtype=\"step\",\n",
    "    density=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.0133448 , 0.04003441, 0.04003441, 0.02668961, 0.06672402,\n",
       "        0.0133448 , 0.0133448 , 0.05337922, 0.10675844, 0.08006883,\n",
       "        0.2802409 , 0.18682727, 0.22686168, 0.37365453, 0.2802409 ,\n",
       "        0.24020648, 0.41368894, 0.36030973, 0.46706816, 0.54713699,\n",
       "        0.61386101, 0.52044738, 0.72061945, 0.8674123 , 0.76065387,\n",
       "        1.00086035, 1.05423957, 0.98751555, 1.20103242, 1.05423957,\n",
       "        1.20103242, 1.32113566, 1.36117008, 1.24106683, 1.49461812,\n",
       "        1.53465254, 1.57468695, 1.708135  , 1.61472136, 1.74816941,\n",
       "        2.14851355, 1.84158304, 1.90830707, 1.84158304, 1.708135  ,\n",
       "        1.66810058, 1.82823824, 1.82823824, 1.85492785, 2.14851355,\n",
       "        1.88161746, 2.05509992, 1.88161746, 2.16185836, 1.89496226,\n",
       "        1.94834148, 1.85492785, 1.96168629, 2.04175511, 1.84158304,\n",
       "        1.90830707, 1.88161746, 1.96168629, 1.81489343, 1.90830707,\n",
       "        1.68144539, 1.88161746, 1.68144539, 1.93499668, 1.46792851,\n",
       "        1.76151422, 1.84158304, 2.12182394, 1.708135  , 1.61472136,\n",
       "        1.62806617, 1.48127332, 1.48127332, 1.41454929, 1.22772203,\n",
       "        1.34782527, 1.17434281, 1.16099801, 1.18768762, 1.4278941 ,\n",
       "        1.45458371, 1.54799734, 1.46792851, 1.45458371, 1.17434281,\n",
       "        0.88075711, 1.05423957, 0.89410191, 0.64055062, 0.44037855,\n",
       "        0.37365453, 0.18682727, 0.10675844, 0.08006883, 0.04003441]),\n",
       " array([0.15182337, 0.16028301, 0.16874266, 0.1772023 , 0.18566195,\n",
       "        0.19412159, 0.20258124, 0.21104088, 0.21950052, 0.22796017,\n",
       "        0.23641981, 0.24487946, 0.2533391 , 0.26179875, 0.27025839,\n",
       "        0.27871803, 0.28717768, 0.29563732, 0.30409697, 0.31255661,\n",
       "        0.32101626, 0.3294759 , 0.33793554, 0.34639519, 0.35485483,\n",
       "        0.36331448, 0.37177412, 0.38023377, 0.38869341, 0.39715305,\n",
       "        0.4056127 , 0.41407234, 0.42253199, 0.43099163, 0.43945128,\n",
       "        0.44791092, 0.45637056, 0.46483021, 0.47328985, 0.4817495 ,\n",
       "        0.49020914, 0.49866879, 0.50712843, 0.51558807, 0.52404772,\n",
       "        0.53250736, 0.54096701, 0.54942665, 0.5578863 , 0.56634594,\n",
       "        0.57480558, 0.58326523, 0.59172487, 0.60018452, 0.60864416,\n",
       "        0.61710381, 0.62556345, 0.63402309, 0.64248274, 0.65094238,\n",
       "        0.65940203, 0.66786167, 0.67632132, 0.68478096, 0.6932406 ,\n",
       "        0.70170025, 0.71015989, 0.71861954, 0.72707918, 0.73553883,\n",
       "        0.74399847, 0.75245811, 0.76091776, 0.7693774 , 0.77783705,\n",
       "        0.78629669, 0.79475634, 0.80321598, 0.81167562, 0.82013527,\n",
       "        0.82859491, 0.83705456, 0.8455142 , 0.85397385, 0.86243349,\n",
       "        0.87089313, 0.87935278, 0.88781242, 0.89627207, 0.90473171,\n",
       "        0.91319136, 0.921651  , 0.93011064, 0.93857029, 0.94702993,\n",
       "        0.95548958, 0.96394922, 0.97240887, 0.98086851, 0.98932816,\n",
       "        0.9977878 ]),\n",
       " [<matplotlib.patches.Polygon at 0x148f18820>])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.hist(out_kde_shifted[0] * out_kde_shifted[1], bins = 100, histtype=\"step\", density=True)\n",
    "plt.hist(np.log(out_kde[0][out_kde[1] == 1]), bins=100, histtype=\"step\", density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 9905., 10172.,  9900.,  9917., 10133., 10005., 10076., 10030.,\n",
       "         9829., 10033.]),\n",
       " array([3.65051872e-06, 1.00002687e-01, 2.00001723e-01, 3.00000759e-01,\n",
       "        3.99999796e-01, 4.99998832e-01, 5.99997868e-01, 6.99996904e-01,\n",
       "        7.99995941e-01, 8.99994977e-01, 9.99994013e-01]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.exp(np.log(np.random.uniform(size=100000))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 10.0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    out[\"rts\"][out[\"rts\"] != -999] * out[\"choices\"][out[\"rts\"] != -999],\n",
    "    bins=40,\n",
    "    histtype=\"step\",\n",
    "    density=True,\n",
    ")\n",
    "plt.xlim(-10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out[\"choices\"][out[\"rts\"] != -999].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.748783111572266\n"
     ]
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "start = time()\n",
    "out_traj = simulator(\n",
    "    model=\"ddm_mic2_multinoise_no_bias\",\n",
    "    theta=[1.0, 1.0, 1.0, 1.5, 0.5, 1.0],\n",
    "    n_samples=100000,\n",
    "    max_t=20,\n",
    ")\n",
    "\n",
    "end = time()\n",
    "\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.685915 ],\n",
       "       [8.714623 ],\n",
       "       [4.737911 ],\n",
       "       ...,\n",
       "       [2.306005 ],\n",
       "       [2.5050144],\n",
       "       [4.278945 ]], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_traj[\"rts\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a4c97370>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SV7Z7LprU1VVNUWVhoaocjjrrPB2ROSXIblenNZkeb7swNU1VDgchBwDq0K1bN1VVVWnTpk2uoPKf//xH+fn56t69u6ST4WHQoEFatWqVdu3apYEDByosLEzl5eV6+eWXFR8f79XLv7t06aKDBw+quLjYdfuWvLw8r22/oRByTKyysFBfJw+XUVbmUb3FalXgfyd2eyLIbie0AICXde7cWSNGjNC9996rl19+WS1bttT06dN18cUXa8SIEa66xMRE/f73v1d8fLxatGghSRo8eLAyMjKUmprq1Z5+9atfqVOnTrrzzjuVnp6uI0eO6OGHH5b042hNY0TIMbEqh0NGWZnsc9IV3LFjnfXMmwFgav/e6zf7ee211zRp0iQNHz5cFRUVGjx4sN599123U0FDhgxRdXW1EhMTXcsSExO1atUqt2XeEBAQoJUrV+qee+5Rnz591LFjR82ZM0c33HCD25zbxsZinG2Cjck5nU5FRESotLTUlPfMKdu1S9+MukmXvv2WrD16NPk+AJjfiRMntH//fsXGxv7448sdjxvEhg0bNHDgQO3bt0+dOnXy+vZr/bP8L09/vxnJAQCYW2S7k4GjET+7yh+sWLFCLVq0UOfOnbVv3z5NmjRJAwYMaJCA4y2EHACA+UW2M13ouNCOHDmiadOm6cCBA2rdurWGDh2quXPn+rqtsyLkAACAOo0bN07jxo3zdRv14tXHOgAAADQWhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKXEIOADC9745+J0d53Q8U9hZbiE0xLWI8rk9MTFRcXJzmzZt3xhqLxaIVK1Zo5MiRHm0zOztbV199tRwOhyIjIz36zDfffKPY2Fht27ZNcXFxHn2mMSPkAABM7buj32nEqhEqq/LsYcXeYA20atWIVfUKOnX57rvvZKvHQ5RByAEAmJyj3KGyqjLNHjRbHSPqfljx+SooLdCMT2bIUe7wasiJjo722raaCubkAACahI4RHdW9VfcGf51rkKqpqVFaWpqioqIUHR2txx57zG29xWLRypUrXe83btyouLg4hYaGKj4+XitXrpTFYtH27dvdPrdlyxbFx8crLCxM/fv3V35+fr36ysnJUd++fRUSEqKYmBhNnz5dVVVVkqTMzExFRkaqurpakrR9+3ZZLBZNnz7d9fl77rlHt99+e7326S2EHAAAGoFly5apefPm2rRpk9LT0/XEE08oKyur1lqn06kbbrhBPXv21NatW/Xkk09q2rRptdY+9NBDmjt3rj777DMFBgbq7rvv9rinQ4cO6frrr1efPn30+eefa9GiRXr11Vf11FNPSZIGDRqkI0eOaNu2bZJOBqLWrVsrOzvbtY2cnBwlJiZ6vE9vIuQAANAI9OrVS48++qg6d+6scePGKT4+XmvXrq21dvny5bJYLHrllVfUvXt3DRs2TKmpqbXWzpo1S0OGDFH37t01ffp0bdy4USdOnPCop5deeknt2rXTggUL1LVrV40cOVKPP/645s6dq5qaGkVERCguLs4VarKzszVlyhRt27ZNR48e1aFDh7Rv3z4NGTLknI7J+SLkAADQCPTq1cvtfUxMjA4fPlxrbX5+vnr16qXQ0FDXsr59+9a53ZiYk3OEzrTdn9uzZ48SEhJksVhcywYMGKCjR4/qX//6lyRpyJAhys7OlmEY+uSTT3TjjTeqW7du+vTTT5WTkyO73a7OnTt7tD9vY+IxAACNQFBQkNt7i8Wimpoar273VFjxxnZPSUxM1J///Gd9/vnnCgoKUteuXZWYmKjs7Gw5HA6fjeJIjOQAAOB3unTpoh07dqi8vNy1LC8vz+v76datm3Jzc2UYhmvZhg0b1LJlS11yySWSfpyX8/zzz7sCzamQk52d7bP5OBIhBwAAv3PbbbeppqZGEyZM0J49e/TBBx/o2WeflSS3U0vn63//93918OBBPfDAA/ryyy+1atUqPfroo5o6daqaNTsZIWw2m3r16qWMjAxXoBk8eLC2bt2qvXv3+nQkh9NVAIAmoaC0wDT7CQ8P1zvvvKP7779fcXFx6tmzp2bOnKnbbrvNbZ7O+br44ov17rvvKjU1Vb1791ZUVJTGjx+vhx9+2K1uyJAh2r59uyvkREVFqXv37iouLlaXLl281k99EXIAAKZmC7HJGmjVjE9mXLB9WgOtsoV4fnfin15yfcpP74kjye2UkST1799fn3/+uet9RkaGgoKC1L59e0knTxn9/DNxcXGnLfupSy+99LT1Q4YM0ebNm8/a/7x58057JMXP79fjC4QcAICpxbSI0aoRqxr1s6vOxV/+8hd17NhRF198sT7//HNNmzZNN998s6xWa4Pu158QcgAAphfTIqbBQ8eFVlRUpJkzZ6qoqEgxMTEaPXq0Zs2a5eu2GhVCDgAAfigtLU1paWm+bqNR4+oqAABgSoQcAABgSoQcAABgSoQcAABgSkw8xgVTUeDZDbICbTYF2e0N3A0AwOwIOWhwgTabLFarClM9uwrAYrWq05pMgg4A4LwQctDggux2dVqTqSpH3TfiqigoUGFqmqocDkIOAK+pLCz06N8gb6nviHRiYqLi4uJOu2vwT1ksFq1YsUIjR470aJvZ2dm6+uqr5XA4FBkZ6dFnvvnmG8XGxmrbtm2Ki4urtWbp0qWaPHmySkpKPNqmJN11110qKSk57S7ODY2QgwsiyG4ntADwicrCQn2dPFxGWdkF22dDjEh/9913stk8f1REQ7nlllt0/fXX+7oNjxByAACmVuVwyCgrk31OuoI7dmzw/TXUiHR0dLTXtnU+rFar3zw6gqurAABNQnDHjrL26NHgr3MNUjU1NUpLS1NUVJSio6P12GOPua23WCxup3s2btyouLg4hYaGKj4+XitXrpTFYjntwZhbtmxRfHy8wsLC1L9/f+Xn59fZS0FBga6++mqFhYWpd+/eys3Nda1bunTpaae/nnrqKbVp00YtW7bUPffco+nTp9d6uuvZZ59VTEyMWrVqpZSUFFVWVtbZy/kg5AAA0AgsW7ZMzZs316ZNm5Senq4nnnhCWVlZtdY6nU7dcMMN6tmzp7Zu3aonn3xS06ZNq7X2oYce0ty5c/XZZ58pMDBQd999d529PPTQQ3rwwQe1fft2XXbZZbr11ltVVVVVa21GRoZmzZqlZ555Rlu2bFH79u21aNGi0+rWrVunr7/+WuvWrdOyZcu0dOlSLV26tM5ezgchBwCARqBXr1569NFH1blzZ40bN07x8fFau3ZtrbXLly+XxWLRK6+8ou7du2vYsGFKTU2ttXbWrFkaMmSIunfvrunTp2vjxo06ceLEWXt58MEHlZycrMsuu0yPP/64vv32W+3bt6/W2hdffFHjx4/Xb37zG1122WWaOXOmevbseVqdzWbTggUL1LVrVw0fPlzJycln/H7eQsgBAKAR6NWrl9v7mJgYHT58uNba/Px89erVS6Ghoa5lffv2rXO7MTEnn8R+pu2ey2fy8/NP23dtvfTo0UMBAQFu262rj/NFyAEAoBEICgpye2+xWFRTU+PV7VosFkmqc7vn8pn69HFqu974fmdDyAEAwM906dJFO3bsUHl5uWtZXl6ez3r5+b591cvPEXIAAPAzt912m2pqajRhwgTt2bNHH3zwgZ599llJP468XCgPPPCAXn31VS1btkxfffWVnnrqKX3xxRcXvI/acJ8cAECT4Onz8/xhP+Hh4XrnnXd0//33Ky4uTj179tTMmTN12223uc3TuRDGjh2rgoICPfjggzpx4oRuvvlm3XXXXdq8efMF7aM2FsMwDF834StOp1MREREqLS1VeHi4r9vxurJdu/TNqJt06dtvydqjh6/b8Yg/9gyg8Thx4oT279+v2NhY14+9We54XJeMjAz95je/UWlpqc9v1verX/1K0dHR+r//+79z3kZtf5anePr7zUgOAMDU6vP8PG+p77OrzsVf/vIXdezYURdffLE+//xzTZs2TTfffPMFDzjHjx/X4sWLlZSUpICAAL3++uv66KOPzniPnwuJkAMAMD0zPj+vqKhIM2fOVFFRkWJiYjR69GjNmjXrgvdhsVj07rvvatasWTpx4oS6dOmit99+W0OHDr3gvfwcIQcAAD+UlpamtLQ0X7chq9Wqjz76yNdt1IqrqwAAgCkRcgAAgCkRcgAAptOELxw2DW/8GRJyAACmcerRAcePH/dxJzhfp/4Mf/44iPpg4jEAwDQCAgIUGRnpevBjWFhYo7jzLjxnGIaOHz+uw4cPKzIy0u2hnvVFyAEAmEp0dLSkup+0jcYtMjLS9Wd5rgg5AABTsVgsiomJUZs2bVRZWenrdnAOgoKCzmsE5xSvh5zq6mo99thj+utf/6qioiLZ7Xbdddddevjhh11DhoZh6NFHH9Urr7yikpISDRgwQIsWLVLnzp1d2/nhhx/0wAMP6J133lGzZs00atQovfDCC2rRooWr5osvvlBKSory8vJ00UUX6YEHHmgU9wwAAPheQECAV34o4b+8PvH4mWee0aJFi7RgwQLt2bNHzzzzjNLT0/Xiiy+6atLT0zV//nwtXrxYmzZtUvPmzZWUlKQTJ064asaOHatdu3YpKytLmZmZWr9+vSZMmOBa73Q6de2116pDhw7asmWL5syZo8cee0xLlizx9lcCAAB+yOsjORs3btSIESOUnJwsSbr00kv1+uuvu55GahiG5s2bp4cfflgjRoyQdPL5G23bttXKlSs1ZswY7dmzR++//77y8vIUHx8vSXrxxRd1/fXX69lnn5XdbldGRoYqKir05z//WcHBwerRo4e2b9+u5557zi0MAQCApsnrIzn9+/fX2rVrtXfvXknS559/rk8//VTDhg2TJO3fv19FRUVuz7SIiIhQv379lJubK0nKzc1VZGSkK+BI0tChQ9WsWTNt2rTJVTN48GAFBwe7apKSkpSfny/HGR7CVl5eLqfT6fYCAADm5PWRnOnTp8vpdKpr164KCAhQdXW1Zs2apbFjx0o6+UAxSWrbtq3b59q2betaV1RUpDZt2rg3GhioqKgot5rY2NjTtnFqnc1mO6232bNn6/HHH/fCtwQAAI2d10dy3nzzTWVkZGj58uXaunWrli1bpmeffVbLli3z9q7qbcaMGSotLXW9Dh486OuWAABAA/H6SE5qaqqmT5+uMWPGSJJ69uypb7/9VrNnz9add97puua9uLhYMTExrs8VFxcrLi5O0sl7HPz8/gZVVVX64YcfXJ+Pjo5WcXGxW82p92e6rj4kJEQhISHn/yUBAECj5/WRnOPHj6tZM/fNBgQEqKamRpIUGxur6OhorV271rXe6XRq06ZNSkhIkCQlJCSopKREW7ZscdV8/PHHqqmpUb9+/Vw169evd7sHQlZWlrp06VLrqSoAANC0eD3k3HDDDZo1a5bWrFmjb775RitWrNBzzz2nX//615JO3qRp8uTJeuqpp7R69Wrt2LFD48aNk91u18iRIyVJ3bp103XXXad7771Xmzdv1oYNGzRx4kSNGTNGdrtdknTbbbcpODhY48eP165du/TGG2/ohRde0NSpU739lQAAgB/y+umqF198UY888oj+93//V4cPH5bdbtdvf/tbzZw501WTlpamY8eOacKECSopKdHAgQP1/vvvKzQ01FWTkZGhiRMn6pprrnHdDHD+/Pmu9REREfrwww+VkpKiq666Sq1bt9bMmTO5fBwAAEiSLEYTfh690+lURESESktLFR4e7ut2vK5s1y59M+omXfr2W7L26OHrdjzijz0DAC4sT3+/vX66CgAAoDEg5AAAAFMi5AAAAFMi5AAAAFPy+tVVaHiVhYWqOsPzuX6qoqDgAnQDAEDjRMjxM5WFhfo6ebiMsjKP6i1WqwK5OSIAoAki5PiZKodDRlmZ7HPSFdyxY531gTabgv57A0UAAJoSQo6fCu7Y0dT3kfH0VBshDgBwJoQcNCqBNpssVqsKU9M8qrdYreq0JpOgAwA4DSEHjUqQ3a5OazI9nlhdmJqmKoeDkAMAOA0hB41OkN1OaAEAnDfukwMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEwp0NcNAOeroqDA49pAm01BdnsDdgMAaCwIOfBbgTabLFarClPTPP6MxWpVpzWZBB0AaAIIOfBbQXa7Oq3JVJXD4VF9RUGBClPTVOVwEHIAoAkg5MCvBdntBBYAQK2YeAwAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEyJkAMAAEypQULOoUOHdPvtt6tVq1ayWq3q2bOnPvvsM9d6wzA0c+ZMxcTEyGq1aujQofrqq6/ctvHDDz9o7NixCg8PV2RkpMaPH6+jR4+61XzxxRcaNGiQQkND1a5dO6WnpzfE1wEAAH7I6yHH4XBowIABCgoK0nvvvafdu3dr7ty5stlsrpr09HTNnz9fixcv1qZNm9S8eXMlJSXpxIkTrpqxY8dq165dysrKUmZmptavX68JEya41judTl177bXq0KGDtmzZojlz5uixxx7TkiVLvP2VAACAPzK8bNq0acbAgQPPuL6mpsaIjo425syZ41pWUlJihISEGK+//rphGIaxe/duQ5KRl5fnqnnvvfcMi8ViHDp0yDAMw3jppZcMm81mlJeXu+27S5cuHvdaWlpqSDJKS0s9/oyvHd+509jdpatxfOdOX7fidzh2AGAOnv5+e30kZ/Xq1YqPj9fo0aPVpk0bXXHFFXrllVdc6/fv36+ioiINHTrUtSwiIkL9+vVTbm6uJCk3N1eRkZGKj4931QwdOlTNmjXTpk2bXDWDBw9WcHCwqyYpKUn5+flyOBy19lZeXi6n0+n2AgAA5uT1kFNQUKBFixapc+fO+uCDD3T//ffrd7/7nZYtWyZJKioqkiS1bdvW7XNt27Z1rSsqKlKbNm3c1gcGBioqKsqtprZt/HQfPzd79mxFRES4Xu3atTvPbwsAABorr4ecmpoaXXnllXr66ad1xRVXaMKECbr33nu1ePFib++q3mbMmKHS0lLX6+DBg75uCQAANBCvh5yYmBh1797dbVm3bt104MABSVJ0dLQkqbi42K2muLjYtS46OlqHDx92W19VVaUffvjBraa2bfx0Hz8XEhKi8PBwtxcAADAnr4ecAQMGKD8/323Z3r171aFDB0lSbGysoqOjtXbtWtd6p9OpTZs2KSEhQZKUkJCgkpISbdmyxVXz8ccfq6amRv369XPVrF+/XpWVla6arKwsdenSxe1KLgAA0DR5PeRMmTJF//znP/X0009r3759Wr58uZYsWaKUlBRJksVi0eTJk/XUU09p9erV2rFjh8aNGye73a6RI0dKOjnyc9111+nee+/V5s2btWHDBk2cOFFjxoyR3W6XJN12220KDg7W+PHjtWvXLr3xxht64YUXNHXqVG9/JQAA4IcCvb3BPn36aMWKFZoxY4aeeOIJxcbGat68eRo7dqyrJi0tTceOHdOECRNUUlKigQMH6v3331doaKirJiMjQxMnTtQ111yjZs2aadSoUZo/f75rfUREhD788EOlpKToqquuUuvWrTVz5ky3e+kAAICmy2IYhuHrJnzF6XQqIiJCpaWlfjM/p2zXLn0z6iZd+vZbsvbo4et2/ArHDgDMwdPfb55dBQAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATImQAwAATCnQ1w0AF1pFQYFHdYE2m4Ls9gbuBgDQUAg5aDICbTZZrFYVpqZ5VG+xWtVpTSZBBwD8FCEHTUaQ3a5OazJV5XDUWVtRUKDC1DRVORyEHADwU4QcNClBdjuhBQCaCCYeAwAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUwr0dQNAY1ZRUOBRXaDNpiC7vYG7AQDUByEHqEWgzSaL1arC1DSP6i1WqzqtySToAEAjQsgBahFkt6vTmkxVORx11lYUFKgwNU1VDgchBwAaEUIOcAZBdjuhBQD8WINPPP7jH/8oi8WiyZMnu5adOHFCKSkpatWqlVq0aKFRo0apuLjY7XMHDhxQcnKywsLC1KZNG6WmpqqqqsqtJjs7W1deeaVCQkL0i1/8QkuXLm3orwMAAPxEg4acvLw8vfzyy+rVq5fb8ilTpuidd97R3//+d+Xk5KiwsFA33nija311dbWSk5NVUVGhjRs3atmyZVq6dKlmzpzpqtm/f7+Sk5N19dVXa/v27Zo8ebLuueceffDBBw35lQAAgJ9osJBz9OhRjR07Vq+88opsNptreWlpqV599VU999xz+p//+R9dddVVeu2117Rx40b985//lCR9+OGH2r17t/76178qLi5Ow4YN05NPPqmFCxeqoqJCkrR48WLFxsZq7ty56tatmyZOnKibbrpJzz//fEN9JQAA4EcaLOSkpKQoOTlZQ4cOdVu+ZcsWVVZWui3v2rWr2rdvr9zcXElSbm6uevbsqbZt27pqkpKS5HQ6tWvXLlfNz7edlJTk2kZtysvL5XQ63V4AAMCcGmTi8d/+9jdt3bpVeXl5p60rKipScHCwIiMj3Za3bdtWRUVFrpqfBpxT60+tO1uN0+lUWVmZrFbrafuePXu2Hn/88XP+XgAAwH94fSTn4MGDmjRpkjIyMhQaGurtzZ+XGTNmqLS01PU6ePCgr1sCAAANxOshZ8uWLTp8+LCuvPJKBQYGKjAwUDk5OZo/f74CAwPVtm1bVVRUqKSkxO1zxcXFio6OliRFR0efdrXVqfd11YSHh9c6iiNJISEhCg8Pd3sBAABz8nrIueaaa7Rjxw5t377d9YqPj9fYsWNd/zsoKEhr1651fSY/P18HDhxQQkKCJCkhIUE7duzQ4cOHXTVZWVkKDw9X9+7dXTU/3capmlPbAAAATZvX5+S0bNlSl19+uduy5s2bq1WrVq7l48eP19SpUxUVFaXw8HA98MADSkhI0C9/+UtJ0rXXXqvu3bvrjjvuUHp6uoqKivTwww8rJSVFISEhkqT77rtPCxYsUFpamu6++259/PHHevPNN7VmzRpvfyUAAOCHfHLH4+eff17NmjXTqFGjVF5erqSkJL300kuu9QEBAcrMzNT999+vhIQENW/eXHfeeaeeeOIJV01sbKzWrFmjKVOm6IUXXtAll1yiP/3pT0pKSvLFVwIAAI3MBQk52dnZbu9DQ0O1cOFCLVy48Iyf6dChg959992zbjcxMVHbtm3zRosAAMBkGvyxDgAAAL5AyAEAAKbEU8gbgcrCQlU5HB7VVhQUNHA3AACYAyHHxyoLC/V18nAZZWUef8ZitSrwJ88DAwAApyPk+FiVwyGjrEz2OekK7tjRo88E2mwKstsbuDMAAPwbIaeRCO7YUdYePXzdBgAApsHEYwAAYEqEHAAAYEqEHAAAYErMyQG8xNPL+5k4DgAXBiEHOE+BNpssVqsKU9M8qrdYreq0JpOgAwANjJADnKcgu12d1mR6dEPHioICFaamqcrhIOQAQAMj5ABeEGS3E1oAoJFh4jEAADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADClQF83ADRFFQUFHtcG2mwKstsbsBsAMCdCDnABBdpsslitKkxN8/gzFqtVndZkEnQAoJ4IOcAFFGS3q9OaTFU5HB7VVxQUqDA1TVUOByEHAOqJkANcYEF2O4EFAC4AJh4DAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABTIuQAAABT8nrImT17tvr06aOWLVuqTZs2GjlypPLz891qTpw4oZSUFLVq1UotWrTQqFGjVFxc7FZz4MABJScnKywsTG3atFFqaqqqqqrcarKzs3XllVcqJCREv/jFL7R06VJvfx0AAOCnAr29wZycHKWkpKhPnz6qqqrSH/7wB1177bXavXu3mjdvLkmaMmWK1qxZo7///e+KiIjQxIkTdeONN2rDhg2SpOrqaiUnJys6OlobN27Ud999p3HjxikoKEhPP/20JGn//v1KTk7Wfffdp4yMDK1du1b33HOPYmJilJSU5O2vBfhURUGBR3WBNpuC7PYG7gYA/IPFMAyjIXfw/fffq02bNsrJydHgwYNVWlqqiy66SMuXL9dNN90kSfryyy/VrVs35ebm6pe//KXee+89DR8+XIWFhWrbtq0kafHixZo2bZq+//57BQcHa9q0aVqzZo127tzp2teYMWNUUlKi999/36PenE6nIiIiVFpaqvDwcO9/eQ+U7dqlb0bdpEvffkvWHj180gMar8rCQn2dPFxGWZlH9RarVZ3WZBJ0AJiap7/fXh/J+bnS0lJJUlRUlCRpy5Ytqqys1NChQ101Xbt2Vfv27V0hJzc3Vz179nQFHElKSkrS/fffr127dumKK65Qbm6u2zZO1UyePPmMvZSXl6u8vNz13ul0euMrAg0myG5XpzWZqnI46qytKChQYWqaqhwOQg4AqIFDTk1NjSZPnqwBAwbo8ssvlyQVFRUpODhYkZGRbrVt27ZVUVGRq+anAefU+lPrzlbjdDpVVlYmq9V6Wj+zZ8/W448/7pXvBlwoQXY7oQUAzkGDXl2VkpKinTt36m9/+1tD7sZjM2bMUGlpqet18OBBX7cEAAAaSION5EycOFGZmZlav369LrnkEtfy6OhoVVRUqKSkxG00p7i4WNHR0a6azZs3u23v1NVXP635+RVZxcXFCg8Pr3UUR5JCQkIUEhJy3t8NAAA0fl4fyTEMQxMnTtSKFSv08ccfKzY21m39VVddpaCgIK1du9a1LD8/XwcOHFBCQoIkKSEhQTt27NDhw4ddNVlZWQoPD1f37t1dNT/dxqmaU9sAAABNm9dHclJSUrR8+XKtWrVKLVu2dM2hiYiIkNVqVUREhMaPH6+pU6cqKipK4eHheuCBB5SQkKBf/vKXkqRrr71W3bt31x133KH09HQVFRXp4YcfVkpKimsk5r777tOCBQuUlpamu+++Wx9//LHefPNNrVmzxttfCQAA+CGvj+QsWrRIpaWlSkxMVExMjOv1xhtvuGqef/55DR8+XKNGjdLgwYMVHR2tf/zjH671AQEByszMVEBAgBISEnT77bdr3LhxeuKJJ1w1sbGxWrNmjbKystS7d2/NnTtXf/rTn7hHDgAAkNQAIzme3HYnNDRUCxcu1MKFC89Y06FDB7377rtn3U5iYqK2bdtW7x4BAID58ewqAABgSg1+M0AAFxaPgACAkwg5gEkE2myyWK0qTE3zqJ5HQAAwO0IOYBI8AgIA3BFyABPhERAA8CMmHgMAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFPiKeRAE1ZRUOBxbaDNxhPOAfgVQg7QBAXabLJYrSpMTfP4MxarVZ3WZBJ0APgNQg7QBAXZ7eq0JlNVDodH9RUFBSpMTVOVw0HIAeA3CDlAExVktxNYAJgaE48BAIApEXIAAIApEXIAAIApMScHgMc8veScy80BNAaEHAB1qu8l51xuDqAxIOQAqFN9LjnncnMAjQUhB4BHuOQcgL9h4jEAADAlQg4AADAlQg4AADAlQg4AADAlJh4DaBDcUweArxFyAHgV99QB0FgQcgB4FffUAdBYEHIAeB331AHQGDDxGAAAmBIjOdB3R7+To7zuUwu2EJtiWsRcgI4AADh/hJwm7ruj32nEqhEqqyqrs9YaaNXzic/LFmo7ax1hCADQGBBymjhHuUNlVWWaPWi2OkZ0PHPdCYemZE/RfR/dV+c2rYFWrRqxiqADj3G5OYCGQMhpIJWFhR5fXdJQPDkNVVB6cv8dIzqqe6vuZ61dNWKVR9ub8ckMOcodhBzUicvNATQkQk4DqCws1NfJw2WU1X0KSDr5D3eg7eyngOqrvqehbCF17z+mRYzHweVUeDoTTmlB4nJzAA2LkNMAqhwOGWVlss9JV3DHM58COqW+Q/CejtB4chpK8m7gsIXYZA20asYnM85ax/wenMLl5gAaCiGnAQV37Chrjx5e3WZ9R2iuanPVBQ0JMS1i6jytxfwenI/6nOJlDg/QtBFy/IynE4WlBhwFKTkoHf/PGVfHSIoJayVFtjtjDfN7UF/1nb8jMYcHaOoIOX7Kk4nCDaLkoLSwr1R5/Ox1QWFSyuYzBh1vzu+ROK3VFNRn/o7EHB4AhJymp45RmDr9e+/JgHPjK1Lry85c8497T+7nLKM5dfF0fo/Eaa2mgvk7AOqDkNOUeDoKU5egMKl9Qt0B5t97z2s3MZJWXb1IjhDrWes4rYWz4R48QNNFyGlk6rpy6qynbuoapfFkFMYTdcy3UVirk0HoH/ee+z7+KyYoTDFnOe31U5zWwk9xDx4AhJxGxNMrp2q9r0195sp4MgpzPiLbnZyPcz6nxaQfT3sdyD3rtmyqrtdpLS5dbxq4Bw8AQk4jctYrp44USydKJUm2oBaKcRZLzuIf13s6SlPXKIy3RLY7//14OCIUExSmVb/JrPO0FpeuNz3M4QGaNkJOI3TalVMlB6WMaxrHKM2F5MmI0H9He2L+/bVi6joFZ2muVb98Wo7Ko6evC42QWraV9OMcny2Ht6hj+Zkv02e0x3yYvwOYCyHHHxz/T+MapbmQ6hoRquf8n5j/vk4TFCbd8n9SWGvZjFBZmwVz1+Ym5Fzm71wyf74Coup+HAqBCPAdQo4/aX2ZZI/zdReNizfm/xz/t/TGHdJfR0n671VdAQFyBDRzrwsMla59Ugq1yVHh1JTPX/D41BdhqHGrz/yd6h8c+tfvfqeD93oWrOsTiCRCEeBNhBz4P2/M//lZUDptxOdUEFo52bWoriAkqX5hqFmwnu89Sbbwdq5TZ7UhDDWM+szfaahAJHGVF+BNfh9yFi5cqDlz5qioqEi9e/fWiy++qL59+/q6rdOc9dLw/04qLjh26OT77/dK5RU/rj/P+83AA54EpXMIQtIZwtDPOAICNKVNa923bU6drTIp2vcaIhBJP17ldXzLFgV7+BlPMUKEpsivQ84bb7yhqVOnavHixerXr5/mzZunpKQk5efnq02bNr5uz6VeD9WsqZHt73dL1dXuK4LCTs4/ge+cQxCSzjIP6GdWlf1bju93SR8/Kf3PI1Jkh9NqCo4d0oydi7Ul/x/q2Pzis2/wJ5OpLxRGmU5Xn0B0Ls/n8hQjRGiKLIZhGL5u4lz169dPffr00YIFCyRJNTU1ateunR544AFNnz69zs87nU5FRESotLRU4eHhXuurbNcufTPqJl369luy9uih3f/ZrVsyb6n90vDv90r/uMf1o2YLaqEYa+vTN2rGScU4XR33O/ouIEAjLolRWbOzjwz5iuuUW/DP/j79LHARhs6ssrDQ45EfT50aIbLPSVdwx7M/2NefMVrVdHj6++23IzkVFRXasmWLZsz48QqYZs2aaejQocrNza31M+Xl5SovL3e9Ly09ed8Zp9Pp1d7Kjh7V0epqOY8eVaXTqaNFB1RdVq2KgiKVW4PcakNKv9MlRyr0dXVXlau7nJXSt5W1bNQpqeigV/tE4xR03So1O3HmH7mnKxxyVtdyGfxPBJQ7Ff3Zs7JUn/B2e2dUEhCgGRe10r0b/1hnbUizYE1u/xu1DGx+ATq78CJCgtTyZ3/XfSqoVApspr1Tf+/rThqUERIkTblFCg87uSAkQrJG+LYpD0QGRygiNNLXbTSIwNatFXTRRV7f7qnf7TrHaQw/dejQIUOSsXHjRrflqampRt++fWv9zKOPPmpI4sWLFy9evHiZ4HXw4MGzZgW/Hck5FzNmzNDUqVNd72tqavTDDz+oVatWslgsPuzMO5xOp9q1a6eDBw969fRbU8CxO3ccu/PD8Tt3HLtz5+/HzjAMHTlyRPY6Tk/6bchp3bq1AgICVFxc7La8uLhY0dHRtX4mJCREISEhbssiIyMbqkWfCQ8P98v/aBsDjt2549idH47fuePYnTt/PnYRERF11jTO2YseCA4O1lVXXaW1a9e6ltXU1Gjt2rVKSEjwYWcAAKAx8NuRHEmaOnWq7rzzTsXHx6tv376aN2+ejh07pt/85je+bg0AAPiYX4ecW265Rd9//71mzpypoqIixcXF6f3331fbthf23iCNRUhIiB599NHTTsmhbhy7c8exOz8cv3PHsTt3TeXY+fV9cgAAAM7Eb+fkAAAAnA0hBwAAmBIhBwAAmBIhBwAAmBIhx8/Nnj1bffr0UcuWLdWmTRuNHDlS+fn5vm7LL/3xj3+UxWLR5MmTfd2K3zh06JBuv/12tWrVSlarVT179tRnn33m67Yaverqaj3yyCOKjY2V1WpVp06d9OSTT9b9HJ4mav369brhhhtkt9tlsVi0cuVKt/WGYWjmzJmKiYmR1WrV0KFD9dVXX/mm2UbmbMeusrJS06ZNU8+ePdW8eXPZ7XaNGzdOhYWFvmvYywg5fi4nJ0cpKSn65z//qaysLFVWVuraa6/VsWPHfN2aX8nLy9PLL7+sXr16+boVv+FwODRgwAAFBQXpvffe0+7duzV37lzZbDZft9boPfPMM1q0aJEWLFigPXv26JlnnlF6erpefPFFX7fWKB07dky9e/fWwoULa12fnp6u+fPna/Hixdq0aZOaN2+upKQknThx4R5Q21id7dgdP35cW7du1SOPPKKtW7fqH//4h/Lz8/X//t//80GnDcQbD8tE43H48GFDkpGTk+PrVvzGkSNHjM6dOxtZWVnGkCFDjEmTJvm6Jb8wbdo0Y+DAgb5uwy8lJycbd999t9uyG2+80Rg7dqyPOvIfkowVK1a43tfU1BjR0dHGnDlzXMtKSkqMkJAQ4/XXX/dBh43Xz49dbTZv3mxIMr799tsL01QDYyTHZEpLSyVJUVFRPu7Ef6SkpCg5OVlDhw71dSt+ZfXq1YqPj9fo0aPVpk0bXXHFFXrllVd83ZZf6N+/v9auXau9e/dKkj7//HN9+umnGjZsmI878z/79+9XUVGR29/fiIgI9evXT7m5uT7szD+VlpbKYrGY5rmOfn3HY7irqanR5MmTNWDAAF1++eW+bscv/O1vf9PWrVuVl5fn61b8TkFBgRYtWqSpU6fqD3/4g/Ly8vS73/1OwcHBuvPOO33dXqM2ffp0OZ1Ode3aVQEBAaqurtasWbM0duxYX7fmd4qKiiTptDvdt23b1rUOnjlx4oSmTZumW2+91W8f2vlzhBwTSUlJ0c6dO/Xpp5/6uhW/cPDgQU2aNElZWVkKDQ31dTt+p6amRvHx8Xr66aclSVdccYV27typxYsXE3Lq8OabbyojI0PLly9Xjx49tH37dk2ePFl2u51jB5+orKzUzTffLMMwtGjRIl+34zWcrjKJiRMnKjMzU+vWrdMll1zi63b8wpYtW3T48GFdeeWVCgwMVGBgoHJycjR//nwFBgaqurra1y02ajExMerevbvbsm7duunAgQM+6sh/pKamavr06RozZox69uypO+64Q1OmTNHs2bN93ZrfiY6OliQVFxe7LS8uLnatw9mdCjjffvutsrKyTDOKIxFy/J5hGJo4caJWrFihjz/+WLGxsb5uyW9cc8012rFjh7Zv3+56xcfHa+zYsdq+fbsCAgJ83WKjNmDAgNNuV7B371516NDBRx35j+PHj6tZM/d/fgMCAlRTU+OjjvxXbGysoqOjtXbtWtcyp9OpTZs2KSEhwYed+YdTAeerr77SRx99pFatWvm6Ja/idJWfS0lJ0fLly7Vq1Sq1bNnSdQ46IiJCVqvVx901bi1btjxt7lLz5s3VqlUr5jR5YMqUKerfv7+efvpp3Xzzzdq8ebOWLFmiJUuW+Lq1Ru+GG27QrFmz1L59e/Xo0UPbtm3Tc889p7vvvtvXrTVKR48e1b59+1zv9+/fr+3btysqKkrt27fX5MmT9dRTT6lz586KjY3VI488IrvdrpEjR/qu6UbibMcuJiZGN910k7Zu3arMzExVV1e7fkOioqIUHBzsq7a9x9eXd+H8SKr19dprr/m6Nb/EJeT188477xiXX365ERISYnTt2tVYsmSJr1vyC06n05g0aZLRvn17IzQ01OjYsaPx0EMPGeXl5b5urVFat25drf/O3XnnnYZhnLyM/JFHHjHatm1rhISEGNdcc42Rn5/v26YbibMdu/3795/xN2TdunW+bt0rLIbBLTYBAID5MCcHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACYEiEHAACY0v8HS/S24eIPq9kAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(\n",
    "    out_traj[\"rts\"][(out_traj[\"choices\"] == 0) & (out_traj[\"rts\"] != -999)],\n",
    "    histtype=\"step\",\n",
    "    bins=40,\n",
    "    label=\"low low\",\n",
    ")\n",
    "plt.hist(\n",
    "    out_traj[\"rts\"][(out_traj[\"choices\"] == 1) & (out_traj[\"rts\"] != -999)],\n",
    "    histtype=\"step\",\n",
    "    bins=40,\n",
    "    label=\"low high\",\n",
    ")\n",
    "plt.hist(\n",
    "    out_traj[\"rts\"][(out_traj[\"choices\"] == 2) & (out_traj[\"rts\"] != -999)],\n",
    "    histtype=\"step\",\n",
    "    bins=40,\n",
    "    label=\"high low\",\n",
    ")\n",
    "plt.hist(\n",
    "    out_traj[\"rts\"][(out_traj[\"choices\"] == 3) & (out_traj[\"rts\"] != -999)],\n",
    "    histtype=\"step\",\n",
    "    bins=40,\n",
    "    label=\"high high\",\n",
    ")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_traj[\"metadata\"][\"trajectory\"]\n",
    "my_traj = out_traj[\"metadata\"][\"trajectory\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.0000000e+00, -9.9900000e+02, -9.9900000e+02],\n",
       "       [ 9.0455776e-03,  2.4773862e-04,  2.5226141e-04],\n",
       "       [-5.6977585e-02,  5.1161140e-04,  4.8763864e-04],\n",
       "       ...,\n",
       "       [-9.9900000e+02, -9.9900000e+02, -9.9900000e+02],\n",
       "       [-9.9900000e+02, -9.9900000e+02, -9.9900000e+02],\n",
       "       [-9.9900000e+02, -9.9900000e+02, -9.9900000e+02]], dtype=float32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_traj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a4c42170>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(my_traj[my_traj[:, 0] != -999, 0], label=\"high\")\n",
    "plt.plot(my_traj[my_traj[:, 1] != -999, 1], label=\"low low\")\n",
    "plt.plot(my_traj[my_traj[:, 2] != -999, 2], label=\"low high\")\n",
    "plt.axvline(out_traj[\"rts\"][0] * 1000 - 1000, color=\"black\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting mock deadline to 999 (this should never have an effect)\n",
      "0.2257390022277832\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "out = simulator(\n",
    "    model=\"ddm_seq2\",\n",
    "    theta=[2.0, 0.0, 0.0, 1.0, 0.5, 0.5, 0.5, 1.0],\n",
    "    n_samples=10000,\n",
    "    max_t=20,\n",
    ")\n",
    "\n",
    "end = time()\n",
    "\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 34., 241., 451., 540., 559., 537., 438., 396., 348., 294., 239.,\n",
       "        168., 142., 105.,  88.,  66.,  61.,  40.,  30.,  30.,  25.,  21.,\n",
       "         20.,   7.,   8.,   7.,   8.,   3.,   6.,   2.,   2.,   1.,   1.,\n",
       "          1.,   0.,   4.,   2.,   1.,   0.,   2.]),\n",
       " array([1.1659404 , 1.35083079, 1.53572118, 1.72061169, 1.90550208,\n",
       "        2.09039235, 2.27528286, 2.46017337, 2.64506364, 2.82995415,\n",
       "        3.01484442, 3.19973493, 3.38462543, 3.56951571, 3.75440621,\n",
       "        3.93929648, 4.12418699, 4.30907726, 4.49396801, 4.67885828,\n",
       "        4.86374855, 5.04863882, 5.23352957, 5.41841984, 5.60331011,\n",
       "        5.78820086, 5.97309113, 6.1579814 , 6.34287167, 6.52776241,\n",
       "        6.71265268, 6.89754295, 7.0824337 , 7.26732397, 7.45221424,\n",
       "        7.63710499, 7.82199526, 8.00688553, 8.19177628, 8.37666607,\n",
       "        8.56155682]),\n",
       " [<matplotlib.patches.Polygon at 0x2a41af130>])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(out[\"rts\"][out[\"choices\"] == 0], histtype=\"step\", bins=40)\n",
    "plt.hist(out[\"rts\"][out[\"choices\"] == 1], histtype=\"step\", bins=40)\n",
    "plt.hist(out[\"rts\"][out[\"choices\"] == 2], histtype=\"step\", bins=40)\n",
    "plt.hist(out[\"rts\"][out[\"choices\"] == 3], histtype=\"step\", bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_traj[\"metadata\"][\"trajectory\"]\n",
    "my_traj = out_traj[\"metadata\"][\"trajectory\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2bb9fe0b0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(my_traj[my_traj[:, 0] != -999, 0], label=\"high\")\n",
    "plt.plot(my_traj[my_traj[:, 1] != -999, 1], label=\"low low\")\n",
    "plt.plot(my_traj[my_traj[:, 2] != -999, 2], label=\"low high\")\n",
    "plt.axvline(out_traj[\"rts\"][0] * 1000 - 1000, color=\"black\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2bb816350>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(my_traj[:, 0], label=\"high\")\n",
    "plt.plot(my_traj[:, 1], label=\"low low\")\n",
    "plt.plot(my_traj[:, 2], label=\"low high\")\n",
    "plt.axvline(out_traj[\"rts\"][0] * 1000, color=\"black\")\n",
    "plt.ylim(-2, 2)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.1367593],\n",
       "       [1.6214037],\n",
       "       [1.8687861],\n",
       "       ...,\n",
       "       [2.0880182],\n",
       "       [3.538052 ],\n",
       "       [1.8469286]], dtype=float32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_traj[\"rts\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "deadline = np.random.normal(size=(1, 10, 1)) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False,  True, False, False,  True,  True, False,\n",
       "       False])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(out[\"rts\"] > deadline)[0, :, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87634164, 5.507736  , 0.845617  , 1.7188146 , 0.94347364,\n",
       "       0.637682  , 4.503843  , 0.8925978 , 0.8825737 , 1.8784422 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out[\"rts\"][0, :, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.64641569,  1.53128978,  1.27339451,  0.84860328,  0.98402066,\n",
       "        2.64141251,  1.62948034, -0.76025468,  0.88753595,  3.33240167])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deadline[0, :, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((10, 10))[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(1, (10, 1)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ssms_dev",
   "language": "python",
   "name": "ssms_dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
